{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a generic code for drawing a state's Home Districts for neutral map estimation\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\n"
     ]
    }
   ],
   "source": [
    "#  copied from vanillaHD-OHOredo  #from NJ 5/4/24 w/minor NY updates, and reducing enclave loop #\n",
    "print(\"This is a generic code for drawing a state's Home Districts for neutral map estimation\") \n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)  \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):  #from IN\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - DO WE NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ??  #\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections count only if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, borderUnits, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     new for MN 4/7/24 - don't enforce contiguity here -- too slow -- fix in cleanup stage instead\n",
    "     \"\"\"\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        tryList = getAdjoiners(addedList,unitNbrs)\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP :  #and wontEnclave(UU, addedList, unitNbrs, borderUnits) :  #we'll post-fix contig'y\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= LEVEL_L :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < MINADJRATIO:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=2):  #TRY 2 FOR OHIO V2.  USUALLY 4\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):  #TRY MAXLOOPS = 3 FOR OH REDO, NOT USUAL 6\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 3, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave\n",
    "\n",
    "def isRookAdj(geo1, geo2):  #intersection with rook (not queen) adjacency\n",
    "    isRookAdj = False\n",
    "    if geo1.intersects(geo2):\n",
    "        if (geo1.intersection(geo2)).geom_type != Point(0,0).geom_type:\n",
    "            isRookAdj = True\n",
    "    return isRookAdj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d041d328-83e0-45db-a869-e0956c23ee26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the state postal code; e.g. OH  OH\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, enter 1 below to use oh_pl2020_vtd.dbf as this state's population data file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "I read in the Census popn data. OH has 11799448 peeps and is divided into 8941 shapes.\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 16 99\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 88 counties in OH .  I will assign them county Nos 0 - 87\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Building county geom for county 20\n",
      "Building county geom for county 40\n",
      "Building county geom for county 60\n",
      "Building county geom for county 80\n",
      "Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 7 unpopulated coastal tracts.  Lets plot them and their parent counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.  Lets plot them and their parent counties\")\n",
    "for t in skipList:\n",
    "    plotPoly(tractGeom[t])\n",
    "    plotCenter(t,tractGeom[t],6)\n",
    "plotPoly(origMAP,0.2)\n",
    "for c in range(nCounties):\n",
    "    if isAlteredCounty[c]:\n",
    "        plotPoly(countyGeom[c])\n",
    "plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 7 units will be axed\n",
      "here is the proposed skipList [8197, 4634, 3730, 4458, 840, 2974, 1008]\n",
      "here is the final skipList [8197, 4634, 3730, 4458, 840, 2974, 1008]\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)\n",
    "#skipList = [1939, 1364] #...  #alter here if needed - 1874, 1270, 1497, 1771 create contiguity problems if axed\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "removing coastal units from countyNo 3\n",
      "removing coastal units from countyNo 17\n",
      "removing coastal units from countyNo 21\n",
      "removing coastal units from countyNo 42\n",
      "removing coastal units from countyNo 46\n",
      "removing coastal units from countyNo 47\n",
      "removing coastal units from countyNo 61\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We appear to have some populated islands.  Separate out the main state geom in next block.\n",
      "we will scale longitudinal distance by 0.76347\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a562a21-4107-4a3c-a320-90442a473387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\n",
      "unit 3739 is a true island.  We will move it to adjoin the mainland in same county.\n",
      "unit 3759 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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lqC1zgIR1QP/ZwNCFd509nIioObFSQ2QPBAFQamotGuxfVr82DAYg+XvjHVhDX7vr5kRE1ow/yYhasp5TgIjrc5+V6kRUVtZjwLxze4DyfKBjjLkjIyJqdkxqiFoyQQBCRwEAlh+uwIeHKpCXl1e3fUUR2FlVnQns00QBEhE1H15+ImrpnI2zf2cWG5CWJ+LatWvw8rphRvCyPGDrC0DmMcCzA+DTGUjdBWQdBwbMqddAf0RE1opJDVFLFxQJse9MLMByjA+VQ370U8Dlb0BRlrHPTd4FYN/bxsQm+B7gzC/AqZ+ADsOBSd8CHYZa+giIiMyCdz8R2YihbWV4uo8DxoQqIIGh9so2g4BR7wJeHYGSXOM0DAoXywRKRFQPvPuJyA6dFdrgga9SsWHNu5g4oi+gbgWU5gJyJ8C9zfUNnT0tFyQRURNiUkNkI3z9fHHubCoy88uAVj2MC1U+lg2KiKgZ8e4nIhvh42WswDR6pm4iohaKSQ2Rjaie1LLRM3UTEbVQTGqIbISHhwcAVmqIyH4xqSGyEdVJzZUruRaOhIjIMpjUENmI6stP2VcaMFM3EZENYFJDZCOqKzUXzp+3bCBERBbCpIbIRlRXakqKiywcCRGRZTCpIbIR1ZUaAKioqLBgJERElsGkhshGVFdqAN7WTUT2iUkNkY2omdTwtm4iskdMaohshKOjo+k5KzVEZI+Y1BDZEAeFAgArNURkn5jUENmQ1m3aAmClhojsE5MaIhvi62OclZuVGiKyR0xqiGyId9VM3azUEJE9YlJDZEOq74BipYaI7BGTGiIbUj0AHys1RGSPmNQQ2ZDqSk0OZ+omIjvEpIbIhlRXarKysi0cCRFR82NSQ2RDqis158+ftXAkRETNj0kNkQ2prtRU6nQWjoSIqPkxqSGyITXnfyotLbVgJEREzY9JDZENqa7UALytm4jsD5MaIhvi5uZmes7buonI3jCpIbIhDg4Opues1BCRvWFSQ2RjVGoNAFZqiMj+MKkhsjHBrdsAYFJDRPaHSQ2RjfHxNk5qyctPRGRvGpXULFmyBIIg4NlnnzUtW7lyJaKioqBWqyEIAvLz8+/azuuvvw5BEGo9QkNDa20TFRV10zYzZ85sTPhENsmT8z8RkZ2SNXTH+Ph4rFixAhEREbWWl5aWIiYmBjExMYiNja1ze507d8auXbuuBya7ObTp06dj4cKFptdOTk4NiJzItlXf1s1KDRHZmwYlNcXFxZg0aRJWrVqFRYsW1VpXXbXZs2dP/QKRyeDr63vHbZycnO66DZG9qx6ALzeXlRoisi8Nuvw0a9YsjB49GkOHDjVbIGfOnIG/vz/atm2LSZMm4eLFizdts379enh6eiI8PByxsbF3HDFVq9WisLCw1oPIHlRXanJ5+YmI7Ey9KzUbN27EkSNHEB8fb7Yg+vbti08//RQhISHIzMzEggULMHDgQJw4cQIqlQoA8OijjyI4OBj+/v44fvw45s+fj5SUFGzevPmWbS5evBgLFiwwW4xELUV1peZyRoaFIyEial71SmrS09MxZ84c7Ny5E0ql0mxBjBw50vQ8IiICffv2RXBwML7++mtMmzYNADBjxgzTNl26dIGfnx+io6ORlpaGdu3a3dRmbGws5s2bZ3pdWFiIwMBAs8VMZK2qKzUXz5+zcCRERM2rXklNQkICcnJy0KNHD9MyvV6Pffv24cMPP4RWq4VUKm10UK6urujYsSNSU1Nvu03fvn0BAKmpqbdMahQKBRQKRaNjIWppak5qKYoiBEGwYDRERM2nXn1qoqOjkZSUhMTERNOjV69emDRpEhITE82S0ADGjshpaWnw8/O77TaJiYkAcMdtiOxRzUkti4qKLBgJEVHzqlelRqVSITw8vNYyZ2dneHh4mJZnZWUhKyvLVGVJSkqCSqVCUFCQ6RdkdHQ0xo0bh9mzZwMAnn/+eYwZMwbBwcHIyMjAa6+9BqlUiokTJwIA0tLSsGHDBowaNQoeHh44fvw45s6di0GDBt10SzmRvbtxpm61Wm3BaIiImo/ZRxRevnw5unfvjunTpwMABg0ahO7du+PHH380bZOWlobc3FzT60uXLmHixIkICQnBQw89BA8PD8TFxcHLywuAcZK+Xbt2Yfjw4QgNDcVzzz2HCRMmYMuWLeYOn6jFc3V1NT3nAHxEZE8EURRFSwfRHAoLC6HRaFBQUMBfrmTzqvvR/PLLLxg2bJiFoyEiarj6fH9z7iciG+ThaaxyslJDRPaESQ2RDQoKCgbAqRKIyL4wqSGyQV5enNSSiOwPkxoiG+Tl6QmASQ0R2RcmNUQ2qHr4BF5+IiJ7wqSGyAaZJrWsMXQCEZGtY1JDZIOqKzU5ubz8RET2g0kNkQ2qrtRcunTJwpEQETUfJjVENqg6qcnOzLBwJEREzYdJDZENqjlTt8FgaLL3sZMByYmohajXhJZE1DLUnNSyoKAAbm5uZm1fFEUcyDwAnV4HESKiAqPM2j4RUUOwUkNkg2pWasw9Vo0oith+fjs6uXeCv4s/Orh1MGv7REQNxaSGyAZpNBrTc3MnNSdyT6C3b2+4Kl2RWZKJVi6tzNo+EVFDMakhskHVs3QD5h2Ar7yyHFmlWfB0NI5YXGmoNFvbRESNxT41RC2MTq9DaWUpskqycKXsimm5KIrwc/ZDa01ryCQy+LUKQOblS2at1JwtOIvu3t0BAKfzTqOjW0eztU1E1FhMaohaiEpDJeIy46A36OGqdIWvky86unU0VWVEUURmSSYOZByAg9QBAQHGpMaclZqc0hx0cOuASkMlLhRewLDgYWZrm4iosZjUELUAmcWZSMpNwr2B90IhVdxyG0EQ4O/iD38Xf1wuvgxVuBMccx3NUqnRG/TYe2kvWqtb42z+WWQUZ2Bgq4GNbpeIyJyY1BBZucScRIgQMbz18Drv08qlFQJ0AVD4KnBcdrxR738i9wSyS7IxoNUAKGVK/HH5DwwOGtyoNomImgI7ChNZucKKQlM/lvpw8nWCNkOLgsqCBr2vVq/FjvM74K50R3RwNJQyJQDAIDbdYH5ERI3BSg2RlRMg3H2jW5B4SVB2vgyyOHm99xVFEb+c/wUj24yETHL9n4kzeWfg4+TToHiIiJoaKzVENirUORSaPhrkXLly941vcOzKMfT3718roblYeBEF2gKEuIeYM0wiIrNhUkNko3w8fFB4tBBXPHKQX55f5/0ScxKhF/XwcPSotfx84Xn08u1l5iiJiMyHl5+IrFzNgfTqw93dHWKFiIxdl3Em/wxKdCWI9I+87d1TFwsv4nTeaYR7hsPX2bfWOlEU2ZeGiKwekxoiK3at/Bpc5C4N2rfmpJbdPbvDIBiQkJ0AnUEHiXC9SCtAQKWhEn4ufhgaPPSWbZ26dgqh7qENioOIqLkwqSGyMh8nfowQtxBEB0fjaM5RDAkc0qB2ak5qmZeXBy8vL0T6RzaorbaubRGfFX9TBYeIyJqwTw2RlXmy65O4VHwJW9K2oLW6dYMvP9Ws1DR2AD6FVAEnmROSc5Mb1Q4RUVNiUkNkhQJcAjAoYBDaubZrcBvOzs6m5+aYKqGHTw9o9VpkFmc2ui0ioqbAy09EViQ1LxXnC8+jt29vaBSaRrVVs8JjrkktyyvLTTN0ExFZG1ZqiKzAuYJz+PXir5BJZBgaPLTRCU211m2MlR5zTWpZpi+DXFr/wfyIiJoDkxoiC9Ib9Pj14q+oNFQiOigarTWtzdq+n78fAPNUas4XnEewKrjR7RARNRUmNUQWcq38GnZe2IkB/gPQwa1Dk7zHhXNnAQBLly6FKIqNaqt67iciImvFpIbIQpJzkxHTJqbJkoUDBw4gIyMDAHDp0iXs2rWr0W0awAH4iMh6MakhspB8bX6Tth8cbLxU5NihHwCgffv2jWrPRe6CEl1Jo+MiImoqTGqILCRQFYj0ovQma9/f3x9Rg4eg7EwcVGoNWrdu3aj2XBxcUFxRbJ7giIiaAJMaIgvp5t0NeeV52HlhJyr0FU3yHk88/hgAoEePHg0exK+mmtMrEBFZG/4LRWRBEV4R6OfXD/su7UNcZpzZR+wdP348vLx9ED1ksFnaK68sN0s7RERNQRAbe0tEC1FYWAiNRoOCggKo1WpLh0N0k/zyfFwuuQwnmRPaaNpYOpxbyi7JxrErx3Bv4L23ne2biMic6vP9zUoNkZVwVbqis0dnXCi8gAJtgaXDuSUfZx8MCRqC3em7LR0KEdFNGpXULFmyBIIg4NlnnzUtW7lyJaKioqBWqyEIAvLz8+/azuuvvw5BEGo9QkNDa21TXl6OWbNmwcPDAy4uLpgwYQKys7MbEz6RVbo34F4cv3IcFwov4Gz+WRRoC1CgLWj0ODPmkF2SjT8v/wl/Z39Lh0JEdJMGz/0UHx+PFStWICIiotby0tJSxMTEICYmBrGxsXVur3PnzrXG0ZDJaoc2d+5c/Pzzz9i0aRM0Gg1mz56N8ePH488//2zoIRBZJUEQMKDVAGw9txURnhFIy0+DTCIz3QIuwNjhVyFVIMQ9xGxTKtzNd2e+Q5hHGAYGDGSHYSKySg1KaoqLizFp0iSsWrUKixYtqrWuumqzZ8+e+gUik8HX1/eW6woKCrBmzRps2LABQ4YMAQCsXbsWYWFhiIuLQ79+/ep9DETWTCJIcF/b+wAAQeqgW25TVlmGlGspyNfmo5VLK7TVtIVUIm2SeNKL0tHRvSNC3UPvvjERkYU06OfWrFmzMHr0aAwdOtRsgZw5cwb+/v5o27YtJk2ahIsXL5rWJSQkQKfT1Xq/0NBQBAUF4cCBA2aLgaglcZQ5opt3N0QFRmHbuW34M+NP/HH5D2w/tx0nr56EQTTf6L+peano7NHZbO0RETWFeldqNm7ciCNHjiA+Pt5sQfTt2xeffvopQkJCkJmZiQULFmDgwIE4ceIEVCoVsrKy4ODgAFdX11r7+fj4ICsr65ZtarVaaLVa0+vCwkKzxUtkbZ7p8Yzpud6gx9Xyq/j57M8YFjwMKXkpCHUPbdDdSgbRgN3puxHiFmLOcImImkS9KjXp6emYM2cO1q9fD6XSfPPVjBw5Eg8++CAiIiIwYsQIbN26Ffn5+fj6668b3ObixYuh0WhMj8DAQLPFS2TNpBIpvJ280du3N17981UUagtxMPNgg9r69eKviPSLRIAqwMxREhGZX72SmoSEBOTk5KBHjx6QyWSQyWTYu3cvPvjgA8hkMuj1erME5erqio4dOyI1NRUA4Ovri4qKipvupMrOzr5tP5zY2FgUFBSYHunpTTccPZE1cpA6oJNHJ4gQTZ2L60MURSikCjjJnZogOiIi86tXUhMdHY2kpCQkJiaaHr169cKkSZOQmJgIqdQ8nRSLi4uRlpYGPz8/AEDPnj0hl8vx66+/mrZJSUnBxYsXERkZecs2FAoF1Gp1rQeRPXFXuiPEPQTOcmc4y53rvf/x3OMI9wxvgsiIiJpGvfrUqFQqhIfX/kfO2dkZHh4epuVZWVnIysoyVVmSkpKgUqkQFBQEd3d3AMbkaNy4cZg9ezYA4Pnnn8eYMWMQHByMjIwMvPbaa5BKpZg4cSIAQKPRYNq0aZg3bx7c3d2hVqvx9NNPIzIyknc+Ed2Br7MvFFIFWrm0qtd+oijibP5ZdPXq2kSRERGZX4PHqbmd5cuXY8GCBabXgwYNAmC8BXvKlCkAgLS0NOTm5pq2uXTpEiZOnIirV6/Cy8sL99xzD+Li4uDl5WXaZunSpZBIJJgwYQK0Wi1GjBiBjz/+2NzhE9kUXydf7EnfAz9nv9uOLVOgLcDh7MMQIKCVSytcLb+KssoyRAVGNWusRESNxbmfiGxccUUx4rPiIZPIEOEVAQepA9Lzc/HHKS0C/LPhINfh3oB7IQgCzhech7+LPxykDpYOm4gIQP2+v81eqSEi6+Li4ILBQYNRaajEidwTOHwhF8t/y0ReRS4eCOuHd/8WZdq2taa1xeIkImosjnVOZCdkEhnOXPTAom+16OQVgkfCh2LrsQIUlOksHRoRkVkwqSGyE6ezi/DqDyfwUK8ArJvaG3OGdoBOb8C3CZcsHRoRkVkwqSGycaIo4uyVYjy04gDcnRyw4P5wCIIAb5USMeG++CLuAgwGu+haR0Q2jkkNkQ0zGETM2ZiIIe/uRX6pDs9Ed4Cjw/XxpKYOaI2zuSX44dhlC0ZJRGQeTGqIbFhusRY/HsvAqC6+OPRSNB7pU3vG757B7hgd4Yc3t55CsbbSQlESEZkHkxoiG+atViKyrQd0ehHe6lvP1/bSqDAUl1fiza1/NXN0RETmxaSGyMZpHOXQVhpuu76VqyNeHh2GDQcvYufJ7GaMjIjIvJjUENk4P1clLl4tueM2k/oGYWiYN1745hiyCsqbKTIiIvNiUkNk43oGu+H81VL8lVl4220EQcDbf+sKB5kEc79KhJ53QxFRC8SkhsjGDe/ki3Zezpj5RQLOXim+7Xbuzg5Y+nA3xJ27iuV705oxQiIi8+DcT0R2IP1aKSatPoj0vFL0DnZHkIcT/DVKDO/si/BWmlrbvrPjFJbvPYtNMyPRI8jNQhETERnV5/ubSQ2RnSjX6fFD4mXsSbmCrMJypOYUQ6szYOo9rTFzUDu4ORsnsdTpDXhoxQHkFmvx8zMDoVbKLRw5EdkzJjW3wKSmYURRRKVBhFx6/UplwoU8TFwVhy+n90PPYP6Sb6m0lXq88fNf+DzuAnoGuWH99L5QyIwD86VfK8Wo939HVKg3PnikGwRBsHC0RGSv6vP9zT41dEev/ZiMDi9vw4nLBaZloiiiotKAj3enWjAyaiyFTIqFY8Px7ZP9cfxSAV79/gSqf+MEujvhzfFdsOVYBr7h3FBE1EIwqaE7Sr9WCgA4l3v9luCIAFcAwK+nciwREplZjyA3LB7fBV8fvoQvDl40LR/T1R8P9gzAaz8m1/r7JyKyVkxq6I6CPZwBAGUVetMyB9n1j03N5XdSrtNj3+krnDjRSnmpFACAV78/AZ3++kB9r9/fGd4qBf6xLh75pRWWCo+IqE6Y1NAdtXJ1BADkFNUekG1MV38AQNzZq3VqZ/ORy3jik0N4a8cp8wZIZuHkIIWiKln97MAF03JnhQwfPtoDV4q0ePrLo7USHiIia8Okhu7IR2OcLyg15/r4JgaDiLSq16t+P1undoaGeQMAVuw9i/1puWaOkhqrV2t3pCwaicmRwXhr+ymcyS4yrQtvpcGyx3oi7uxV/OeXFAtGSUR0Z0xq6I58qyZBPH7J2FF4a1ImBr2zGyerRqfdn3YVx9Lz79qOt1qJsd2M1Z25XyXyUoaVih0VBj+NEot+rj255YD2nnh2aEes2ncWv53i/FBEZJ2Y1NAdVSc1Z3NLkFdSgafWH8HV4gp8Ma0v3n+kGwBg7Ed/ouIOEyZWu3C1FJ4uDiir0OPfPyQ3ZdjUQEq5FLEjw7D39BXsSandEXzmve0wJNQHT284escpF4iILIVJDd2Rt1pheu7oYBzDZEioN+7p4Imx3Vrhn/e2BQBMWh2H2w15JIoiVv9+Fonp+ejX1gMLx4bjx2MZ+Pl4ZtMfANXbiM4+CG+lvunSolQi4P1HuqG1pzOmfRqPnEJOfElE1oVJDd2RUi41PXeQSjCpbxBOZV3/lR47Mgxjuvoj/nweZnyegP1pufj8wHm8tf0UWr/4Mxb9dBIvf38Ci37+CzPvbYcPHumOsd38MTLcF698n3RTB2SyPEEQ8GifYPyZevWmjuDOChnWTO4NvShi+meH63z3GxFRc2BSQ3V2taQCfdq4I+1KSa0+Mf+b2B0fPtodO09m49FVB/HqD8lYtsc4IeLqP85h85FLWDi2M14cGQqJRIAgCFj0QDikEgEvfpt02woPWc6Yrn4AgEPnrt20zlejxJrJvXE6uxjzvk7kbfpEZDVklg6ArJ9KKUNReSWyC8vRumrcmkt5ZXB1cjBtc1+EPwa298KGQxfRxtMZfdu44+vD6Wjt6YwB7T3hoqj9UfNwUeCtCRGYtu4wvjyUjkf7BjXrMdGdqZRyjO/eCp/8eQ6T+gbBw0VRa314Kw3ef6Qb/vlFAv7zSwpeiAm1UKRERNexUkN31S3QFQCQWVBu6mNzpUh703YaJzmejGqHmHBfuDk74J/3tsOIzr43JTTVosN8MLFPEP7vp5M4n1uCnKJyHEi7itScYpzLLcGx9Hx8m3CJl6gs5KXRYRBF4M2ttx5baHhnX7w0Mgwf70nD14fTmzk6IqKbsVJDdzUy3A+/n8nF94mXUVCmAwD8lVWIwaHejW77ldFh2J+Wi6j/7LntNu29XfDT0/fU6t9DTc/TRYGnotrh3V9O441x4bc8//8Y2AZnc4vx8ndJCHRzQmQ7DwtESkRkxEoN3VV01cB5Px/PRP+qLy293jz9KJwVMqx6ohfae7ugT2t37Jo3CF/N6IeNM/rhh1kD8Nnf++ByXhn+s4ODvtWF3iCatY/SoI5eqNAbbrq9u5ogCFg4Nhx92rhj5hcJOHul+JbbERE1ByY1dFc+aiUe6hUAALj/wz9xb0cv/HLSfAOwdfRRYde8e7FxRj+091ahb1sP9Gvrga6BrhjU0Qv/GNgGXx66iMv5ZWZ7T1tToq3Eq9+fQOfXtmPOxkSzJTahvioEuDliy/HM297pJJdK8PGknvB0ccDjaw4hg39PRGQhTGqoTv7vgXAAQG6xFntPX0Fmgfn7uUgkwi2XTx3QBm7ODrjvg9/x1PoErP3zHPaevoJynf3eTlxRaUBBqfFS4J6UHHRd8Au+ik/HgHae+PFYhtmmMxAEAeO7t8LPxzMxcVXcbbfTOMrx6dQ+AIApaw9xxGgisggmNVQnCpkU+18cYnqdW3xzR+Gm4u7sgE0zI/FQr0BcKdJiwZaTmPzJIczecKTZYjAHg0G8ZQfr+rpWUoHhS/ei56KdmPHZYUxZG49Kg4gfZg/Amim98beeAfhodxqSMwrMEDUwb3gIXh4VhsT0/DuOSxPo7oR1f++D3OIKTP00HiXaSrO8PxFRXTGpoTrzd3XEoZeiAQB927hj58lsvLn1L6w/eAHp10qb9L39NI6IHRWGTTP748irw/B/Yztj11852HzkUpO+rzmIooiPdqdi2NK96P3GLuxq5KW7Lw9dxKW8Mgzr5GO6DPjGuHCE+akBAPNjQtHG0xkTV8bh4lXz/L1UGkQo5RLTqNK3097bBeum9sGZ7GLM2nAE2kr7raYRUfPj3U9UL+U64xxPB89dw8EbBmbb96/BCPJwavIY3J0d8Fi/YMSdu4Yl205hRGdfON/mtnFrsCM5G+/sSMHADp5Iu1KC0npeNqvUGyCTSnAutwQJF/JwIO0qKg0iZgxqi7f+FoErRVq083Ixbe+lUuCV0WGYtu4wjlzMM8vfyYWrJSjXGaA3iJDe5jJhtS4BGix7rAemfXoY//w8AZ9M7n3bS4tEROZkvd8EZJXcnOU3LQtyd8KIzj5o5ebYbHEIgoDYkaEY8u5evP5jMt7+WwQEwTq/ONftP49+bd0xqW8wfj+Ti24BrnXar6hch3//kIwfEi/Dw0VR69JVZ381PF0UUCvlUCtr/53kFJZj3tfH0Ke1O0Z18TPLMfiolXB2kN41oak2sIMXPpjYDU+uP4IFW5Lx+v2drfbvh4hsB5Maqpecqi/Wfw5qi4EdvBDqp4LnDaPNNpcANye8MjoM//4hGc4KGV6/v7NF4riT/NIKHDp/DQvHdsbe01fQ1tP5jpWTc7kl+CYhHX+cycWxSwVwkErQPcgNWQXlmBwZjDA/NR7o3uquY/a4KGT4K7MQ205kYmy3Vo0+jpwiLVp7Otdrn5hwPyx6IBwvf3cCGicHzB3agYkNETUpJjVUZ6IoYt3+83CQSjBnaAc4OVj+4zM4xBtAMtp5u9x1W0v47VQO9AYRQ8N88PHuNAzr5HPL7c7nluBf3xzD4Qt5cHaQwd3ZAU8PaY9RXfxMfWXqylutxLZnB+LV709gzsZEXMorw6zB7QEAheU6qBSyeicXl/JKEdCAStykvsHIL9XhnR0pxsrTfZ2Y2BBRk7H8txK1GBvj0/HZgQt4fUwnq0hoAGPnZU8XBfadvoIHewZY3ajDO09mo2ugK4rKdbicX4Z7Q7xuud2sDUeQnGGc/fzwK0MbfRxqpRzvP9Id/q6OWLrzNALcHCGVCJi94Sg0jnKM696qXpWtS3llGNLAEaRnDW4PtaMcr35/AgCY2BBRk7GObyZqEQ6kXQUATBnQxsKRXCeVCFg4tjPmbDyKh1fG4f2Hu9X7MklTKa2oxN7TVzBrcHv8cSYXMomAvm3cARhv767uPFuircSVIi1kEgHfPTXArInZvGEdkVOoxZyNiQCM48l08HbBp/vPY0xXP3T0UeFKkRbBHs6m/jLfJFzC6ewi+KiV8NcokVlQjnO5JbU6I9fX4/2CAQCvfn8CEkHAK6PDmNgQkdkxqaE6OXoxDzuSszB9oPUkNNVGdfFDgJsjHvjoT0T9Zw++nN7PKuYg+iU5G6UVeoyJ8MfCn06iS4AGTg4yxJ29ikdWxsFfo8QnU3vjX5uOo0RbiZ3z7kUbMydkcqkE/3kwAq3cHLHzZDbemtAFTg5SPLIyDhOWHTBt1zPYDS+NCsW+07l4/9cz8FYpUFCmg7bSeLebIAAPVo0q3VCP9wuGKIr49w/JkAjAS6OY2BCReTVqnJolS5ZAEAQ8++yzpmUrV65EVFQU1Go1BEFAfn5+o9sEgKioKAiCUOsxc+bMxoRPd1Gu02P0B7/jkZUHMGHZfoT6qvDs0I6WDuuWIgJcsf3ZQQjxUWHiqjj8cSbX0iFh24lM9AhyhZdKgT0pObh4tRR7UnJMFZGMgnLEvPc7Ll4rxdczI82e0FQTBAHzhnXEtjkDERHgivbeKsTFRmPzU/3x7oNd8fGkHjiXW4IJyw7gg9/OYE50Bxx6eShO/V8Mjrw6DC/EhOCjR3tALm38sFZPRLbG62M6YdXv57B01xmzzlNFRNTgSk18fDxWrFiBiIiIWstLS0sRExODmJgYxMbGmqXNatOnT8fChQtNr52cmn5MFHv2r2+Om/p5vDEuHA/1CjTLF1tT6eijwurJvTDw7d14bM1BvP9IN9zf1d8i1YCKSgP2p17FtIFt8PuZK6g0iLhaUoEpa+Nx6v9iIBGAtl4uCHRzxNxhHdHZX9Os8cmkEvQIckOPIDcAQFSIF87llsDd2QF+GmOHYEEQ4O7sgKei2pv1vacMaIOSCj3e2ZECvcGA54eHsGJDRGbRoKSmuLgYkyZNwqpVq7Bo0aJa66orLHv27DFbm9WcnJzg6+vbkJCpjorKdVj0018Y1NELW45lAAC+nzUA3QJdLRtYHQW6OyH+5aF4/cdkzNmYiB8SM7DogXD4uzbNGDqiKCL9Whk2H72ExPR8eKsUmDO0I64Wa1GkrUTv1u747ujlWvvM2XgUgiDgjQfC0bet5S+TAYCTg6xZE6vqu7He2ZGCEq0e/76vEwfoI6JGa9DP7lmzZmH06NEYOnSo2QKpS5vr16+Hp6cnwsPDERsbi9LS2w8Br9VqUVhYWOtBNyutqETSpQIkXLiGrUmZGPyfvfjqcDpmVc2r9Oa4Li0moanmpVLgo0k9sOLxnjhxuQDDl+7D53EXYDCY91JHfmkFpn+WgEHv7MaqfWchEQRsOZaJpzccgZ/GEU4OUhw+n4cON9xuviM5Gx9O7G41CY2lzBrcHoseCMe6A+fxyg8nzP73Q0T2p96Vmo0bN+LIkSOIj483WxB1afPRRx9FcHAw/P39cfz4ccyfPx8pKSnYvHnzLbdfvHgxFixYYLYYbY0oivjPLylY++d5lNaYpHBgB0881i8cxy/lY0r/NvBSWWZgPXMY0dkX/dp6YMm2v/Dq9yewJTEDiyd0adRdPNXySirw2JqDuJxfhqUPd8WQUB9oHOX46XgGZm84iiH/2YPSCj3SrhTjg4ndMaC9J97bdRptvVwwvkcrhPrWb+wZW/VYv2A4yCSY/+1xFJdXYunD3eo8ajER0Y3qldSkp6djzpw52LlzJ5RKpVkCqGubM2bMMD3v0qUL/Pz8EB0djbS0NLRr1+6m7WNjYzFv3jzT68LCQgQGBpolZlsQfz4PH+1Ow98HtMGYrn64VlIBF4UMvVq7QyoRMKKzbVzm0zjKsXh8BMZ09Ufs5iSMfP93zInugBmD2jaof1BpRSXW7b+AlfvSIBEEbJzRr1aCcl+EPySCgKfWGytdoyOM0xSEt9Jg9eTe5jkoG/NQr0A4O8jwzMajqKg04MNHu0NmxX23iMh6CWI9bj/4/vvvMW7cOEil18fR0Ov1EAQBEokEWq3WtG7Pnj0YPHgw8vLy4OrqapY2ayopKYGLiwu2b9+OESNG3DX2wsJCaDQaFBQUQK2271/JOr0BT60/gpSsIux5Pspu+jKUVejx3q7TWPX7WbT3dsGMQe0wpqsfFLK6jQuz7/QVzP/2OLIKy/FY32A8GdXutn11rhZrIZUIcHVyMOch2LTtJzIxe8NRRIV44X8Te9x1RnAisg/1+f6uV6UmOjoaSUlJtZZNnToVoaGhmD9//i2Tj6ZqMzExEQDg52eeCftsnd4g4puEdFzOL8efqbk4lp6Pdx/qajcJDQA4OkgROyoMoyP8sHTnaTy/6RiWbPsLD/cOxJiu/ujorbrt+didkoOpa+NxT3tPrP9HX7S9yyUsDwvNh9WSxYT7YdVkKZ764ggeX3MQayb3hsbp5glUiYhup15JjUqlQnh4eK1lzs7O8PDwMC3PyspCVlYWUlNTAQBJSUlQqVQICgqCu7txNNXo6GiMGzcOs2fPrlObaWlp2LBhA0aNGgUPDw8cP34cc+fOxaBBg257+zddtz81F3O+SjTN8hzmp8bGGf3Qq7W7hSOzjIgAV6yd2gdpV4qxbv95fH7gAj7anQaVQob2Pi7wclHAw0UBLxcHeKoUcHVywCd/nEM7L2d8Pq0Pbz9uQoNDvLFhel/8/dN4TFi+H9/O7M/EhojqzOwjCi9fvrxWB91BgwYBANauXYspU6YAMCYpubl1HxzNwcEBu3btwnvvvYeSkhIEBgZiwoQJeOWVV8wauy1KuJCHf3x2GG29nPG/id0R6qviJZEq7bxcsHBsOF4eHYZD567h+KUCnMstQW6xFkmX85FbVIGrJVro9CJ81Ap88Eh3JjTNoHuQG774R19MWn0Qj66Ow5rJveGrMU8fPiKybfXqU9OSWXufmoQLeXj2K+Nkgw90a4XxPQLg7ty45OPP1FzM/CIBYb5qrPt7H/ZRaABRFFFQpoOjg7TOfW/IPP7KLMS0T+OhF0WsfqI3ugQ07wCFRGQd6vP9zaTGCpTr9JiwbD/yS3XoGqjBzpPZEEXgng6eGNXFDyM6+dYqwReU6nDsUj4iAjTQ6UUYRBH703Jx+HwenBykOHD2Ks7nlqJYW4l72nti2WM9oFKyhE8tT05hOaZ/dhins4ux9OFuiAm3jbvyiKjumNTcgrUmNaIoYv63x/F9YgY2P9kf4a00uFZSgZ+OZ+Dn45k4dP4apIKAnsFu6N3aHSqlDJ/uP4/MgvLbttnK1RF927pjeCcfDOvky3E/qEUr1+nx3NfH8HNSJmJHhmLGoLa8DEhkR5jU3II1JjU/JF7GOztScCmvDO/8LQIP9rp5HJ2cwnJsT87C72dyEXf2KorKKzGwgyeeHx6Cs7nFEEVg7Z/nkZFfhkUPhCO7sBwP9w7ipSayKQaDiHd3puCj3Wn4W88ALHogHEo5P+NE9oBJzS1YW1Lz+YHz+PePyRjeyQcTegRgeB0Gu9NXDSPPygvZq02H0/HqDyfQ2V+DZY/1gLeKHYiJbF19vr85bKcFrP79LF79IRlT+7fBskk965TQAMZkhgkN2bMHewXiy+n9kH6tFPf/708cS8+3dEhEZEWY1DSzM9lFWPTzXxjeyQev3hdmV4PfEZlD9yA3bHn6HvhqlHhwxQF8m3DJ0iERkZVgUtPMzuaWAAB6BLuxsyNRA/molfjqn/3wQDd/PLfpGBZsSUal3mDpsIjIwsw++B4ZGQwiDpy9imJtJa6VVOCP1Fw4O0jx9WHjr8pQX5WFIyRq2RQyKd6aEIHwVhos3HISKVlF+PDRHo0e34mIWi52FG4iD684gIPnrtVaFuqrQlF5JZ4a3A6T+gY3eQxE9iLu7FU8tf4InBykWPl4L3Tyt/zNAERkHrz76RaaI6nJKijHgbO5+OSP80i6XAAASHhlKLILtfDTKOHGX5BETeZyfhlmfHYYaVeK8c7fumJMV39Lh0REZtBks3TT7X156CJiN1+fbdxbpcCyx3rCo2pyRCJqWq1cHfHNzP54cfNxPP3lUZzJKca8YR0tHRYRNSNWaszg4z2peHt7CgCgSysNPFwcoKwxT5CIm0/xjWf9xi1u/lsR77j+5v3FO673USnxxrhwyKTsK062RRRFzP0qEd8nZiDtzVEcBoGoheM4Nc2sOoEJ81PDzdkBBhEo0+lNj3KdAdrK2o8KvQG6Go9KvQF6g2h6GEQRYq1H7fcUBEAQBNNDIgimcWykEgEyqQTyGg8HqQQKmfFx4nIBvjqcjoz820+1QNRSCYKAe0O8AAAXr5VaOBoiak6s1NihhAt5mLBsP36ZOwgdfXgXFtmecp0ekYt/hbuzA5Y91pOfc6I7KNfpsWTbKTzWLxjtvV3qtP3yvWmo1It4fkRIk8fHPjV0R45Vc+aUVegtHAlR01DKpVgyIQKLt/6FsR/+iQX3d8bfegZwsEuiWziWno9P95/Hp/vP4x/3tIG7iwMq9WLVlQSxxlUFERWVBsSdvYrL+WUA0CxJTX0wqbFDSrnxqmO5jkkN2a4RnX0xsIMnXvn+BF749jjWHTiPl0aFYUB7T0uHRmRVLlw1XqYd370VdpzMQnF5JWRV3RZkUqFGVwYBMomA/u08IJUI+Dkp08KR34xJjR2qnsG7jEkN2TgnBxn++1A3PNonCG9s/QuTVh/E4BAvvDQqDB14SYrsnMEgIuFiHv7zSwq6Bmjw34e71Xnfzw+cx+Yjl5suuAZiUmOHqjs2l+s4rDzZh16t3bH5yf7YdiILS7adwn3/+wNvjuuCCT0DLB0aUbM7fikfL393An9lFqLSICIiQIPVk3vXqw2lXIqKqptcrOkuWiY1dqi6UsPLT2RPBEHAqC5+GBLqjVe/P4HnNh3DsUv5eGV0JzjIrOcfZaKmtO/0FUxbF48QXxVeGR2G9t4q9GvrXu/ExPQ9UmmAC5MasiSFjH1qyH4p5VK8/bcIdA10xYItyTiZUYiPJ/WAt1pp6dCImtS3CZfw3KZjaO3hhM1PDmhUMl9d8S+r0MNFYT2phPVEQs1GEAQo5RL2qSG7JQgCHusXjDA/NZ78IgH3/e8PPNI7EJUGEc9Ed4BSLr17I0QtQH5pBXan5GBrUhZ2nsxG/3YeeP3+zo2uTlprxZ9JjZ1SyqXsU0N2r2ewG3565h7MWn8EH/yWCgDwUikwdUAbC0dG1HB5JRX45WQWfk7Kwv7UXFQaRGNl8v7OeCIyGILQ+KENqhN/a/txzKTGTjnKpVb3YSSyBG+VEhum98PRi/mYveEIvj96GRN6BkCtlFs6NKJ6Sc0pxtJdp7HjRBb0oog+rd3x2phOGN7ZFz5mvrxqreOdMamxU0q5FFomNUQAALlUgj5t3PHmuC6Y+1UiRizdhzfHd8HgEG9Lh0Z0WwaDiOSMQnx39DJ2JGfhcn4Z/DVKvDI6DKMi/OCtarp+YtY6NAiTGjulZKWG6CZDO/lg+9xBePHb45i6Nh7TB7bBiyPDOCkmWQ2DQcTmo5fxnx0pcFZIkXalBE4OUvytZwAi23pgcKh3s/QJc+TlJ7ImSrnE6jp4EVmDVq6O+OzvfbD2z/NY9PNJnM4uxrsPdYWni8LSoZEdO3IxDxsPXcSBs1eRfs04RcED3fwxb1gIBod6wcmheb/Oq5Oacl5+Imtg7FPDjsJEtyIIAv5+Txu093YxXY56Y1w4RnT2NUsnS6L62H4iC899nQgXpQzDOvlgUAcvRIV4W3R8perLT6VMasgaGO9+sq4PI5G1GdTRCzvmDsJLm5Mw84sjAIA2ns4Y3tkHc6I7NPuvY7IvBaU6vLH1JL4+fAkxnX3xzoMRUFlJB3YHmXEuqJKKSkuHUov1DANIzcqRSQ1RnXi6KLDi8Z54bUwnAIBEAFb/fg793vwVr3yfhIJSnYUjJFsjiiK2JmVi6NK92JaUhcXju2DZYz2sJqGp5qyQoajcupIa/sywUwq5BOVFTGqI6kIQBEwd0MY0fk36tVJ8tDsVX8RdxBdxF7Hn+Si09nS2cJRkC87nlmDBlmTsTrmCoWHeWPRAF/hqrHO0a6kVXoplpcZOcZwaooYLdHfCkgkR+O9DXQEAY/73BzYeugiDQbRwZNRSFZXrsHjbXxi2dC9OZxfjfxO7Y9UTvaw2oQEAnd4AmZXdGchKjZ3iiMJEjTe+RwBCfdX4787TeHFzEj7ek4aewW7oFuiKroGuCPVVccoFuiODQcS3Ry7h7R0pKCrXYfbgDpgxqK2pI641k0klqLSyRJ5JjZ1ylEutbiRIopaok78aqyf3wqFz17A1KRNH0/Px0/EM6PQi1EoZHu4diPBWGvRt42HVv7qp+SVcyMOCLck4fqkA93f1x4sjQ+Hv6mjpsOpMIZNAW2ldP46Z1NgppVwCbSWTGiJz6dPGHX3auAMwTvJ3KqsIW45l4PvEDKz6/RwkAjCwgxcm9w/GkFAfC0dLlpRVUI53dqTg2yOXEN5KjU0zI9G7tbulw6o3hUxidSPTM6mxU0pWaoiajFIuRbdAV3QLdMWr93VCXkkFtidn4av4dPz908N4cWQoZt7bztJhkgV8tDsV7+86A0cHKRaP74KHegW22BGrlXIpKzVkHaqnSRBFkYOJETUxN2cHTOwThEd6B2LpztNYsu0Uissr8dzwjg36/08URRgKCyHI5ZA4OTVBxGRuF66W4I2f/8IvJ7MxY1BbzB7SvsVPmmq8/GRdP45595OdcpRLYRABnd66OnkR2YJly5YhIiICarUaarUakZGR2LZtGwRBwPiOClx46z78KyYUEokEgiCYHps2bTK1UXN59WPjxo0oS05G8e7dqDh/Hu+99hpCgoLg6OiIkJAQfPbZZxY8arqVg2evYuraQ4j6zx4kpufjw0e746VRYS0+oQEAhcz6bjhhpcZOKWtMRmbJobaJbFFAQACWLFmCDh06QBRFrFu3DmPHjsXRo0cRGhqKzMxM/Jh4Ge/sSIGnSoGI4sPYsOpDjBw5slY7a9euRUxMjOm1c1kZxLx8qIYMwbJly/D6ihVYuXIlQq9dwyl3d0yfPh1ubm4YM2ZMcx8y3cBgEPHfnafx4e5UhPmpsWR8F9zftVWLuKuprhRW2DezUd9mS5YsgSAIePbZZ03LVq5ciaioKKjVagiCgPz8/Ea3CQDl5eWYNWsWPDw84OLiggkTJiA7O7sx4ds1RwfjX721dfIisgVjxozBqFGj0KFDB3Ts2BFvvPEGXFxcEBcXB6lUCl9fX8yI6YmdL49FKz8/fLr+a3QeMALOzrUH8HN1dYWvr6/poT92DE49ugMAPv/8c/zzn//EuL59ETp4MB555BHMmDEDb731liUOmWooKtdhxueH8dGeVMyPCcXWZ+7Bw72DbCqhAYyVGq2VVWoanNTEx8djxYoViIiIqLW8tLQUMTExeOmll8zWJgDMnTsXW7ZswaZNm7B3715kZGRg/PjxDQ3f7ill1jltPJGt0ev12LhxI0pKShAZGVlrXWtPZzymTkVFzln01mXg659/r7V+1qxZ8PT0RJ8+ffDJJ59A6uZmWqfVaqFQKKBNOQ1lWBgAwNHREYcOHYJOx6kbLKGgVIdFP53EgCW/Ie7sNXwyuTeejGpns/0WjZUaG0hqiouLMWnSJKxatQpuNf4nA4Bnn30WL774Ivr162e2NgsKCrBmzRr897//xZAhQ9CzZ0+sXbsW+/fvR1xcXEMOwe4pq34xWNv1UCJbkZSUBBcXFygUCsycORPfffcdOnXqVGubv/7Yg7dfexV+bq5o7emOC1/8F++++R5yruRh4cKF+Prrr7Fz505MmDABTz31FFbu3Ant2bMAgBEjRmD1smVIUblAFEUcPnwYq1evhk6nQ25uriUO2W4ZDCK+PpyO6P/uwVfx6Xi0bzB2zB2EwaHelg6tSSll1jeHYIOSmlmzZmH06NEYOnSo2QK5U5sJCQnQ6XS11oWGhiIoKAgHDhy4ZXtarRaFhYW1HnQdKzVETSskJASJiYk4ePAgnnzySUyePBknT540rc+9eB4/vPc2jl3OwdwXYzFt5ZfQt+sFw7HfsPbpaUB+OTJzitG5czjmz5+PF154Ae+tW4fKnByUp6Tg+QcewIghQ9A/KgpyuRxjx47F5MmTAQASCfvJNZfzuSV4cMUBvPDNcfRv54md8+7FiyND0aoFDaLXUNZYqal3R+GNGzfiyJEjiI+PN1sQd2szKysLDg4OcHV1rbXcx8cHWVlZt9xn8eLFWLBggdlitDWOpkoNkxqipuDg4ID27dsDAHr27In4+Hi8//77WLFiBQDg2M7tOJmTB51ejylTp8JVo0LsG6/g/MVMfLvha1ScPIILG/+HxVs2wa9vFCK6dsOlS5cg694dRz5ci/MuPlj71ddYra9EdnY2/Pz8sHLlSqhUKnh5eVny0O1CuU6Pn45nYsGWZHg4O+DL6f0Q2c7D0mE1K2u8pbteSU16ejrmzJmDnTt3Qqk0z3DfTdEmAMTGxmLevHmm14WFhQgMDDRb+y2dUm78JcdKDVHzMBgM0Gq1AIBKnQ4nf9+N41eu4f7776+VhLQO8sNzL84BAPzy65849OlyFP22EZtS0uHo6IQ3Zs/FXocIXFA54IfVcegW6IbRXfwQIJVi48aNuO+++1ipaUI6vQE/JGbg/V9PI/1aGWI6++LtByNs4hbt+mrxt3QnJCQgJycHPXr0MC3T6/XYt28fPvzwQ2i1Wkil9evdXZc2fX19UVFRgfz8/FrVmuzsbPj6+t6yXYVCAYVCUa9Y7Ilj1S3dvPuJyPxiY2MxcuRIBAUFoaioCBs2bMCePXuwY8cOAMDvGz5FRk4OjqecwZKl79+0/5YtW5CdnY1+/frhkddew9p1X2Dv5h2I6tgGmsKLeGR4FIpVTlj+zQ/43bUtvt5aicDLu3HixAmsW7euuQ/XLhSU6fD7mStYuvM00q6UYGiYDz6Z3BsdfFSWDs1irHG6nXolNdHR0UhKSqq1bOrUqQgNDcX8+fPrndDUtc2ePXtCLpfj119/xYQJEwAAKSkpuHjx4k13E1Dd1BynhojMKycnB0888QQyMzOh0WgQERGBHTt2YNiwYQCAzDMpOHQuHR5qNbq0Cb5pf7lcjo8++ghz586FKIpo3749np85HW7ZF9D7/gcx8NEHcerUKWxYvAOX/jqFLEiQGtwVr3z0NYKCbm6PGkYURRy5mIfVv5/DthPGrg59WrvjfxN7oJO/2sLRWZ5C1sKnSVCpVAgPD6+1zNnZGR4eHqblWVlZyMrKQmpqKgDjHQAqlQpBQUFwdzdO2BUdHY1x48Zh9uzZdWpTo9Fg2rRpmDdvHtzd3aFWq/H0008jMjKy3ndZkZGiasA9aysdEtmCNWvW3HH9Q68tRud7o3Hw+2+x8d//QqvQCAye/Hf4tDX2wYmJiak16F5u+gV89sIz6BpzPwZNMnYGDgsLw9GjRwEYx0VZvO0UPj54EQkFcXhrQgRaezrf/MZUZ8kZBXjth2QcvpCHNp7OeG1MJwwJ9UawB89rNYVMYnX9Ms0+ovDy5ctrddAdNGgQAOPImFOmTAEApKWl1fuWw6VLl0IikWDChAnQarUYMWIEPv74Y7PFbW8EQYBSLuGklkQWIJPL0XXYSHSJHo4zBw/gjy+/wPqX5qHPAw8h8m+PQCqr/U/zb5+ugdrTB/c+NvWW7amUcrw5rgvu6+KH+ZuPI+b9fXh+eAimDmjTYidLbG55JRX47uhlHE3Px6FzV5FdqEWAmyPWTO6FwSHekPA83qR6QktrmkNQEEXRLib/KSwshEajQUFBAdRqlg0BoPvCXzB9UFs8FdXe0qEQ2TWDXo8Dm7/CwW83wiOgNe6b+y94tLp+Y8OaObMQFN4Zw6Y/dde2Sisq8c6OFHy6/zy6Bbri7QkRdt3v424yC8rw2YEL+PzABVToDYhopUGPYDd0C3TF0DAfTiNzB98mXMJzm47h9KKRTXqe6vP9zbmf7JhSLkU5KzVEFieRSjHgwUfRrnsvbHnvP/jshWdw72N/R/eY+yAIAgx6HWTyuv1z7eQgw2tjOmN0Fz+88M1xjP7gD9Os0NV96choW1Im/vXNcegNIh6PDMY/B7WFhwtvMKkrRdVdtOWV1jOHIJMaO+Yol6Lcyjp5Edkz3/YdMeXdD7Dns7XY/ekKnEs8ilGzn4UgkUJfWVmvtnq1dsfWOQPxwa9nsGJfGj7cnYq2ns7YOmeg3SY3JzMK8eHuM/DTOOLguas4cbkQo7r4YvH4CGgc7e+W7MaqHsRVqzMA5huRpVGsI7Uii1DIpexTQ2Rl5Aolhk1/EmP/9SoyUpKx8qm/oyTvCgpy6j/1gVIuxQsxodjy9D0IcnfC2dwSRL+7F5sOp0NvsIueBya7U3Iw7uM/cSy9AD8ey0CwuzNWPN4TH07swYSmgaorNdZ0WzcrNXbMUW59PdeJyKh9r774+3vLcXzXdlz66xTadOtx951uI9RXjX0vDEZqThHe/eU0/vXNcSzfm4Yno9rj/q7+VnPpwNyuFGnxnx0pSLiYh9ScYkSHeuOjST3stlJlbgqZ9c0hyKTGjinlUo5TQ2TFnF3dEPm3iWZrr723Csse64njl/Lx3q4zeH7TMby9/RTCW2nwTHQHdAt0Ndt7WZLBIGLL8Qws3HISV0sqMLFPEJ4e0h73RfjzbjAzUrJSQ9bEUW59Q1wTUdOLCHDFJ1N6IzWnCKt/P4eN8en47VQOHukdiEl9g9ElQGPpEBtEFEXEnb2Gxdv+wvFLBYgO9cYz0R3Q1UaSNWtTXamxpgH4mNTYMaVcioIynaXDICILae+twpIJEXjlvk74Iu4CPv3zPDbGp6OzvxoP9QpETLgvfNRW0gP0NgwGEYmX8rHjRBa2J2fhwtVSRARosHFGP/Rra18TTDa364O4slJDVkAplyK7sNzSYRCRhbkoZJh5bzv845422Hv6CjbGp2PhTyfx2o/JmHZPG0zp3xqB7k6WDtNEpzfg0Llr2H4iC7+czEJ2oRYezg4Y1skHC8eGY2B7Tw6W1wyq+yaxUkNWQSmXsE8NEZnIpBJEh/kgOswHBaU6LN72Fz47cB5r/jiH8d1bYUxXfxRpK9HawwkdfVTN2uG2XKfH72dysf1EFnb9lY2CMh1auTpidBd/jOjsg16t3dlfpplVV2q0VtSNgUmNHTP2qWFSQ0Q30zjJsWRCBF4eHYbvj17G/35Lxeajl03r5VIBfdq4Y3CIN6JCvNHOy9nsQ+Xnl1Zg7+kr2JGchT0pV1BaoUd7bxc83i8YIzr7IryV2mqG57dHvKWbrIqSHYWJ6C5USjkej2yNSX2DcSmvDC5KGS5cLUFiej72pFzB2ztSsOjnvxDk7oTBIV5o76OCVqeHxlEOf1dH+GmU8NM4wtHh9lWdikoDLl4rxdkrxUi7UoKzV4pxOrsISZcLYBCBiAANZg1ujxGdfdHe26UZj57uRFFz8D0rwaTGjjk6sFJDRHUjkQgI8jD2q3F3dkD3IDdMHdAGZRV6HDibi92nrmDXXzn4LO4CnORSlNwwsKdaafy6cXSQwkulgEImRUWlAZfzy3CtpMK0nYtChrZezmjv5YJH+wZhUEcv+Gkcm+9Aqc6kEgFyqcBKDVkHhYx9aoiocRwdpBgS6oMhoT5YKIqo0BugkBl/MGUVlCOjoAyZ+eXIKdJCEICyCj2uFGtRUWmAXCpgaJgP/DRKBLo7oZ2XM7xUCl5SakEUMik7CpN1qK7UWNO08UTUcgmCYLokoZRL0drTGa09nS0cFTUlpZWNTG+bY2NTnShlUhhEoEJvPVk2ERG1HNZWqWFSY8eqO+6xszARETWEQiZhUkPWoXreDmsqHRIRUcuhsLKhQZjU2LHqgbOs6QNJREQth0ImsapbupnU2LHqpIZ3QBERUUMYLz9Zz3cIkxo75ihnnxoiImo4axvElUmNHTNVaiqsJ8smIqKWg5UashqmSo0VfSCJiKjlUMh5SzdZCdPdT6zUUAu0ePFi9O7dGyqVCt7e3njggQeQkpJSa5uVK1ciKioKarVx4sP8/HzLBEtko5QyDr5HVoIdhakl27t3L2bNmoW4uDjs3LkTOp0Ow4cPR0lJiWmb0tJSxMTE4KWXXrJgpES2SyG3rnFqOE2CHVPIJBAEdhSmlmn79u21Xn/66afw9vZGQkICBg0aBAB49tlnAQB79uxp5uiI7ANHFCarIQgClDIpKzVkEwoKCgAA7u7uFo6EyH4oePmJrIm1TUZG1BAGgwHPPvssBgwYgPDwcEuHQ2Q3lFbWUZiXn+yco5UNcU3UELNmzcKJEyfwxx9/WDoUIrtiHFHYer5DmNTYOSWTGmrhZs+ejZ9++gn79u1DQECApcMhsisKmQTlVlSp4eUnO6eUs08NtUyiKGL27Nn47rvv8Ntvv6FNmzaWDonI7ijlUlRUGiCKoqVDAcBKjd0z9qmxniybqK5mzZqFDRs24IcffoBKpUJWVhYAQKPRwNHREQCQlZWFrKwspKamAgCSkpKgUqkQFBTEDsVEZqCoGu9MW2kwDRNiSazU2DlHB1ZqqGVatmwZCgoKEBUVBT8/P9Pjq6++Mm2zfPlydO/eHdOnTwcADBo0CN27d8ePP/5oqbCJbIpCZkxkrKWzMCs1dk4pk1pVJy+iuqpLufv111/H66+/3vTBENmp6pHptTo94Ci3cDSs1Ng9JSs1RETUQNZWqWFSY+eUMuuaNp6IiFoOhay6T411/DhmUmPnHB0kKOOElkRE1ADVnYOt5ccxkxo7p5RJUW4lGTYREbUsrNSQVXF0kKKclRoiImoAU58aW6jULFmyBIIgmGbCBYCVK1ciKioKarUagiAgPz//ru0sW7YMERERUKvVUKvViIyMxLZt22ptExUVBUEQaj1mzpzZmPAJVSMKW0kHLyIialmq736ylop/g5Oa+Ph4rFixAhEREbWWl5aWIiYmBi+99FKd2woICMCSJUuQkJCAw4cPY8iQIRg7diySk5NrbTd9+nRkZmaaHm+//XZDw6cqSrmUfWqIiKhBrK1S06BxaoqLizFp0iSsWrUKixYtqrWuumqzZ8+eOrc3ZsyYWq/feOMNLFu2DHFxcejcubNpuZOTE3x9fRsSMt2GUi5BeaUeoihCEARLh0NERC1IzRGFrUGDKjWzZs3C6NGjMXToUHPHA71ej40bN6KkpASRkZG11q1fvx6enp4IDw9HbGwsSktLb9uOVqtFYWFhrQfdzFEuhShazweSiIhajuqOwtYyMXK9KzUbN27EkSNHEB8fb9ZAkpKSEBkZifLycri4uOC7775Dp06dTOsfffRRBAcHw9/fH8ePH8f8+fORkpKCzZs337K9xYsXY8GCBWaN0RZV346n1VnHvB1ERNRyCIIAB5nEan4Y1yupSU9Px5w5c7Bz504olUqzBhISEoLExEQUFBTgm2++weTJk7F3715TYjNjxgzTtl26dIGfnx+io6ORlpaGdu3a3dRebGws5s2bZ3pdWFiIwMBAs8ZsCxyrEpkynR4aWH6IayIialkUMonV3NJdr6QmISEBOTk56NGjh2mZXq/Hvn378OGHH0Kr1UIqbdivfQcHB7Rv3x4A0LNnT8THx+P999/HihUrbrl93759AQCpqam3TGoUCgUUCkWDYrEn1ddDraV0SERELYtSbj0j09crqYmOjkZSUlKtZVOnTkVoaCjmz5/f4ITmVgwGA7Ra7W3XJyYmAgD8/PzM9p72qGalhoiIqL5abKVGpVIhPDy81jJnZ2d4eHiYlmdlZSErKwupqakAjH1lVCoVgoKC4O7uDsCYHI0bNw6zZ88GYLxUNHLkSAQFBaGoqAgbNmzAnj17sGPHDgBAWloaNmzYgFGjRsHDwwPHjx/H3LlzMWjQoJtuKaf6uT7EtXV8IImIqGVRyCQt+5buO1m+fHmtDrqDBg0CAKxduxZTpkwBYExScnNzTdvk5OTgiSeeQGZmJjQaDSIiIrBjxw4MGzYMgPHS1K5du/Dee++hpKQEgYGBmDBhAl555RVzh293WKkhIqLGMA7iah3fIYIoiqKlg2gOhYWF0Gg0KCgogFqttnQ4ViOroBz9Fv+KtVN6Y3Cot6XDISKiFmb8x3+inZcL3nmwa5O0X5/vb879ZOdYqSEiosZQyKRWc0s3kxo7x7ufiIioMZRy6+kozKTGzilkEggCKzVERNQwCpn13NLNpMbOCYIApRV9IImIqGVRsFJD1sTRQcrLT0RE1CBK9qkha6KUSZjUEBFRgyjkEqup9jOpISgdpCirYFJDRET1Z00jCjOpIWOfGiv5QBIRUcuilEutZkRhJjUERwcpyiqs4wNJREQtCys1ZFWUcgkKynSWDoOIiFoghcx6KjVmn/uJWp6KSgOK7zAjOhER0e0Yb+m2jqSGlRqCh7MCShk/CkREVH9KmRQVegP0BstPJclvMoLGUW41WTYREbUs1dPtVFjB9wiTGrKq0iEREbUsiqpKvzV0FmZSQ1bVc52IiFoWhVwKAFYxAB+TGrKqMQaIiKhlYaWGrAorNURE1FAKmbFSYw3dGJjUkFVNG09ERC2LsqqjsDXMIcikhqC0omnjiYioZWGlhqyKQiaFTi9axRgDRETUspj61FhBxZ9JDZnGGGC1hoiI6ktpuvvJ8t8hTGroeunQCrJsIiJqWa7/MLb8dwiTGjJ9IMtZqSEionriLd1kVZSs1BARUQM5SCUQBFZqyEpYU+mQiIhaFkEQoJBJ2KeGrEN16dAaPpBERNTyKGRSq/hhzKSGTD3XreEDSURELY9SLrGKLgxMaoiVGiIiahSFTGoVN5swqSGrGg2SiIhaHoWMlRqyEtY0bwcREbU8SrmUt3STdWClhoiIGsN495Plv0OY1BDkUgESgZUaIiJqGIWVTIzMpIYgCAKUcimTGiIiahClTMpKDVkP4/VQy38giYio5WGfGrIq1jIaJBERtTwKuXV8hzCpIQCs1BARUcM5yqUoY1JD1oKVGiIiaiilXIqyCst/hzCpIQBgR2EiImowR7kNdBResmQJBEHAs88+a1q2cuVKREVFQa1WQxAE5Ofn37WdZcuWISIiAmq1Gmq1GpGRkdi2bVutbcrLyzFr1ix4eHjAxcUFEyZMQHZ2dmPCpxqUcusYY4CIiFoeZUvvUxMfH48VK1YgIiKi1vLS0lLExMTgpZdeqnNbAQEBWLJkCRISEnD48GEMGTIEY8eORXJysmmbuXPnYsuWLdi0aRP27t2LjIwMjB8/vqHh0w2MM6xa/gNJREQtj7VU+2UN2am4uBiTJk3CqlWrsGjRolrrqqs2e/bsqXN7Y8aMqfX6jTfewLJlyxAXF4fOnTujoKAAa9aswYYNGzBkyBAAwNq1axEWFoa4uDj069evIYdBNbBSQ0REDaWs6igsiiIEQbBYHA2q1MyaNQujR4/G0KFDzR0P9Ho9Nm7ciJKSEkRGRgIAEhISoNPpar1faGgogoKCcODAAbPHYI+sJcsmIqKWx1EuhUEEKvSW/XFc70rNxo0bceTIEcTHx5s1kKSkJERGRqK8vBwuLi747rvv0KlTJwBAVlYWHBwc4OrqWmsfHx8fZGVl3bI9rVYLrVZrel1YWGjWeG2NUiZFOW/pJiKiBlBUTYysrTSY5hO0hHpVatLT0zFnzhysX78eSqXSrIGEhIQgMTERBw8exJNPPonJkyfj5MmTDW5v8eLF0Gg0pkdgYKAZo7U9SrkEWlZqiIioAWQS4yUnvV60aBz1SmoSEhKQk5ODHj16QCaTQSaTYe/evfjggw8gk8mg1zf8S9HBwQHt27dHz549sXjxYnTt2hXvv/8+AMDX1xcVFRU33UmVnZ0NX1/fW7YXGxuLgoIC0yM9Pb3BsdkDBQffIyKiBpJU9aPRi5ZNaup1+Sk6OhpJSUm1lk2dOhWhoaGYP38+pFLzlZwMBoPp8lHPnj0hl8vx66+/YsKECQCAlJQUXLx40dTv5kYKhQIKhcJs8dg6JQffIyKiBpJJjUmNwdCCkhqVSoXw8PBay5ydneHh4WFanpWVhaysLKSmpgIw9pVRqVQICgqCu7s7AGNyNG7cOMyePRuAsaoycuRIBAUFoaioCBs2bMCePXuwY8cOAIBGo8G0adMwb948uLu7Q61W4+mnn0ZkZCTvfDITBTsKExFRA1VXaipbUlJTF8uXL8eCBQtMrwcNGgTAeAv2lClTAABpaWnIzc01bZOTk4MnnngCmZmZ0Gg0iIiIwI4dOzBs2DDTNkuXLoVEIsGECROg1WoxYsQIfPzxx+YO324prWQ0SCIianmk1X1qLJzUCKJo4QtgzaSwsBAajQYFBQVQq9WWDsfqfBF3Af/+4QTS3hxl0TEGiIio5TmQdhUTV8Xht+fuRVsvF7O2XZ/vb879RACMlRqDCOgs3HOdiIhaHmeFsU9tqYUntWRSQwCMt3QDQDmnSiAionpyVhh7s1g6qTF7nxpqmZRVgyUdOnsNvholHB2kUMqlcJBK4CCTQCGTwEEqgUTCS1NERFSbs4MxnSjRVlo0DiY1BADwURsHU/zHZ4fvuJ1MIkBeleg4VCU6Clnt17d6rpBJjPvVXH+L/W/cxrRfzfZueA+ZlAVHIiJLUppGFGalhqxAlwAN4l8eioIyHcoq9CjT6VGu06Oi0oAKvcH4Z6UB2hrPjev0NZ4boK21zoBibWXtNvS111c/b8xtgBIBpsSnuqIkvyH5kUuvr6uVQNVIrG5M1uRSATKpBFKJALlUgFQigVwiQCoRIJMKkEkkkElu3KZquVQwrZNV7SOXSCCtXl61jJ2yicgWVE+NYOlBXJnUkImXSgEvlWUGLNQbROj0NyQ9t0mGdPqbkyjdjcnSDe3UbLtYW4mKEsNN76fTi1Xt6VGhN6BSLzb5mAumhOc2iZEpGbrduhuSruqESSaVwKEqKZPf8FwuFar+NLbjcJvn1VUzmWn76/vd+JzJGZF9c5BVVWosPDQIkxqyClKJAKnE2I/HmoiiCL3BmNxUGkTo9SJ0BsP1ZXpD1Z8iKg3XEyF9zXVVy/UGETqDCL3BmEDV3MaY1N2w7oY2rm9z43sbUK4Ta7yH8f10emPiVmkQoas0oKIqRl2lATqDiAoz/qKqTs5kEgESiYC7pThSiVBVWZPCQWZMxvSG6/Ebz7MxoXSUS6GUS+Aol0Ihl9Z67egghUImhSjWOO8GEQaDWGu4dgEwJV5CzYUABAgQhFqLIAjG5bcikQiQSgCpYDzW6j8lwp2XSwShVsJYnRjKJDWeVyWWshuTT8n1fWQ1kk0Zq31kJap/XPHyE5EVE4SqS03WlWuZRc2ErboyVV3xqjRcT4p0+trPK/XVVa7az/V6A/QioDfcOVkSReOoozUrcJV6Q61LddXPAaBcd/1yaLnOYLo8ml+qMy0TBFRVrySQCoBMIjEmJoLx/QDAlOKYXoumeGquF0URt6vPiSJgqDpveoNoei6Kxjlv9DUSKr2h6hzXWF59Xs1ZALyeHAlVCWLN5Kg6Waqq1kmMl2blkpsTLFOyJBFu2KZ20nVjQuYgq2r3ltvVjKu6XYmp6siEzLYoZJafQ5BJDZGdqpmwWVuFzNZVX26trqLpDLUTRuPz6mSzKrGsqrJdTzir2qiRYFYni5WG2sno9bau71um06NSW7Odm5PXG6t7Or0B5hyu9VYJmVwmVCU+NyRdt0jIHG5IoKqTs1slZLfe53pCVp0AixBR9Z/ptWh6XZXw1lx+wzqxagPxTm3cYX/ctH3t16jxPjXfs7oN1NjHINbeH7Xe/y5t3yIG1Hif6rYNVQ0bRNHUJcCSmNQQETWz6sutAIAWNu+u3lA7WdLpqxKeqmSqorIqEdLfKmESq7a5c3JWc1tdpXhT0lem00NXXiNZq7FdzffS1UgKK/QtcxoY46VQ448QATUujZqWX7+EWnM71Hxd47mkaqVwh/1N73uHtiVVzyXC9cu3Hbxd0DXAtflOzi0wqSEiojqz1v5vd3O7y63VCZAoijclDqa+VsLNSUX1Fz1ueC0xJRFVicdtkpJabd8icah+T6ofJjVERGTzeLnVPnDUMiIiIrIJTGqIiIjIJjCpISIiIpvApIaIiIhsApMaIiIisglMaoiIiMgmMKkhIiIim8CkhoiIiGwCkxoiIiKyCUxqiIiIyCYwqSEiIiKbwKSGiIiIbAKTGiIiIrIJdjNLtyiKAIDCwkILR0JERER1Vf29Xf09fid2k9QUFRUBAAIDAy0cCREREdVXUVERNBrNHbcRxLqkPjbAYDAgIyMDKpUKgiA0yXsUFhYiMDAQ6enpUKvVTfIeto7nsHF4/hqP57BxeP4ah+fvZqIooqioCP7+/pBI7txrxm4qNRKJBAEBAc3yXmq1mh/GRuI5bByev8bjOWwcnr/G4fmr7W4VmmrsKExEREQ2gUkNERER2QQmNWakUCjw2muvQaFQWDqUFovnsHF4/hqP57BxeP4ah+evceymozARERHZNlZqiIiIyCYwqSEiIiKbwKSGiIiIbAKTGiIiIrIJTGrq6fTp0xg7diw8PT2hVqtxzz33YPfu3ab1x44dw8SJExEYGAhHR0eEhYXh/fffv2u7R44cwbBhw+Dq6goPDw/MmDEDxcXFTXkoFtFU5+9u7dqKpjh/e/bsgSAIt3zEx8c39SE1u6b6DALAzz//jL59+8LR0RFubm544IEHmugoLKepzl/r1q1v+vwtWbKkKQ/FYpryMwgAWq0W3bp1gyAISExMbIIjsF5MaurpvvvuQ2VlJX777TckJCSga9euuO+++5CVlQUASEhIgLe3N7744gskJyfj5ZdfRmxsLD788MPbtpmRkYGhQ4eiffv2OHjwILZv347k5GRMmTKlmY6q+TTF+atLu7aiKc5f//79kZmZWevxj3/8A23atEGvXr2a69CaTVN9Br/99ls8/vjjmDp1Ko4dO4Y///wTjz76aHMcUrNqqvMHAAsXLqz1OXz66aeb+nAsoinPIQC88MIL8Pf3b8pDsF4i1dmVK1dEAOK+fftMywoLC0UA4s6dO2+731NPPSUOHjz4tutXrFghent7i3q93rTs+PHjIgDxzJkz5gneCjTV+Wtouy1NU52/G1VUVIheXl7iwoULGxWvNWqqc6jT6cRWrVqJq1evNmu81qYpP4PBwcHi0qVLzRWq1Wrq/4+3bt0qhoaGisnJySIA8ejRo+YIu8VgpaYePDw8EBISgs8++wwlJSWorKzEihUr4O3tjZ49e952v4KCAri7u992vVarhYODQ62JuhwdHQEAf/zxh/kOwMKa6vw1tN2WpqnO341+/PFHXL16FVOnTjVH2Falqc7hkSNHcPnyZUgkEnTv3h1+fn4YOXIkTpw40RSHYTFN/RlcsmQJPDw80L17d7zzzjuorKw0Z/hWoSnPYXZ2NqZPn47PP/8cTk5O5g69ZbB0VtXSpKeniz179hQFQRClUqno5+cnHjly5Lbb//nnn6JMJhN37Nhx221OnDghymQy8e233xa1Wq147do1ccKECSIA8c0332yKw7CYpjh/DWm3pWqq81fTyJEjxZEjR5ojXKvUFOfwyy+/FAGIQUFB4jfffCMePnxYnDhxoujh4SFevXq1KQ7DYprqM/juu++Ku3fvFo8dOyYuW7ZMdHV1FefOnWvu8K1CU5xDg8EgxsTEiP/3f/8niqIonjt3zi4rNUxqRFGcP3++COCOj7/++ks0GAzi/fffL44cOVL8448/xISEBPHJJ58UW7VqJWZkZNzUblJSkujp6Wn6kN3J+vXrRR8fH1EqlYoODg7i888/L/r4+IhLlixpikM2K0ufv/q2a20sff5qSk9PFyUSifjNN9+Y8xCbnKXP4fr160UA4ooVK0zLysvLRU9PT3H58uVmP15zs/T5u5U1a9aIMplMLC8vN8chNjlLn8P3339fHDBggFhZWSmKIpMau5aTkyP+9ddfd3xotVpx165dokQiEQsKCmrt3759e3Hx4sW1liUnJ4ve3t7iSy+9VK9YsrKyxKKiIrG4uFiUSCTi119/3ejja2qWPn/1adcaWfr81bRw4ULRy8tLrKioaPRxNSdLn8PffvtNBCD+/vvvtZb36dOn3n8HlmDp83crJ06cEAGIp06davBxNSdLn8OxY8eKEolElEqlpgcAUSqVik888YRZj9Waycx3Iavl8vLygpeX1123Ky0tBYBafV+qXxsMBtPr5ORkDBkyBJMnT8Ybb7xRr1h8fHwAAJ988gmUSiWGDRtWr/0twdLnr67tWitLn79qoihi7dq1eOKJJyCXy+u8nzWw9Dns2bMnFAoFUlJScM899wAAdDodzp8/j+Dg4PocikVY+vzdSmJiIiQSCby9vRu0f3Oz9Dn84IMPsGjRItPrjIwMjBgxAl999RX69u1b18No+SydVbUkV65cET08PMTx48eLiYmJYkpKivj888+LcrlcTExMFEXRWCr08vISH3vsMTEzM9P0yMnJMbVz8OBBMSQkRLx06ZJp2f/+9z8xISFBTElJET/88EPR0dFRfP/995v9GJtSU52/urRrC5ry8yeKxooXqkrktqopz+GcOXPEVq1aiTt27BBPnTolTps2TfT29havXbvW7MfZVJrq/O3fv19cunSpmJiYKKalpYlffPGF6OXlZZMVhqb+/7gaLz9RncTHx4vDhw8X3d3dRZVKJfbr10/cunWraf1rr712y2upwcHBpm12794tAhDPnTtnWvb444+L7u7uooODgxgRESF+9tlnzXhUzaepzt/d2rUVTXX+RFEUJ06cKPbv37+ZjsRymuocVlRUiM8995zo7e0tqlQqcejQoeKJEyea8ciaR1Ocv4SEBLFv376iRqMRlUqlGBYWJr755pstpj9NfTXl/8fV7DWpEURRFJuwEERERETULDhODREREdkEJjVERERkE5jUEBERkU1gUkNEREQ2gUkNERER2QQmNURERGQTmNQQERGRTWBSQ0RERDaBSQ0RERHZBCY1REREZBOY1BAREZFNYFJDRERENuH/AQuhYD2r6JtRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 3068 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 3097 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 1006 is a true island.  We will move it to adjoin the mainland in same county.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RUN THIS ONLY IF WE FOUND POPULATED ISLANDS\n",
    "nSubGeoms = len(MAP.geoms)\n",
    "subGeoms = [MAP.geoms[n] for n in range(nSubGeoms)]\n",
    "subAreas = [subGeoms[n].area for n in range(nSubGeoms)]\n",
    "mainSubGeomNo = subAreas.index(np.max(subAreas))\n",
    "mainGeom = subGeoms[mainSubGeomNo]\n",
    "nonMainGeom = dummyPoly\n",
    "for geo in subGeoms:  #the \"nonMainGeom is all pieces of the state not in the biggest piece\n",
    "    if geo != mainGeom:\n",
    "        if nonMainGeom == dummyPoly:\n",
    "            nonMainGeom = geo\n",
    "        else:\n",
    "            nonMainGeom = nonMainGeom.union(geo)\n",
    "        \n",
    "print(\"Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\")\n",
    "hasIsland, isIsland = [False for c in range(nCounties)], [False for c in range(nCounties)]\n",
    "isIsland =  [False for t in range(nTracts)]\n",
    "islandXshift, islandYshift = [0. for t in range(nTracts)], [0. for t in range(nTracts)]\n",
    "islandList = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(nonMainGeom):  #this county contains an island.  Find all its island tracts\n",
    "        hasIsland[c] = True\n",
    "        if countyGeom[c].disjoint(mainGeom):  #need to connect the whole county\n",
    "            print(\"county\",c,\"is completely disconnected from mainland.  Move it to adjoin mainland\")\n",
    "            isIsland[c] = True\n",
    "            countyDist = [999999. for cc in range(nCounties)]  #default, to avoid picking island counties as closest to this one\n",
    "            for cc in range(nCounties):\n",
    "                if countyGeom[cc].intersects(mainGeom):\n",
    "                    countyDist[cc] = countyGeom[c].distance(countyGeom[cc].intersection(mainGeom) )\n",
    "            closestCounty = countyDist.index(np.min(countyDist))\n",
    "            p1, p2 = nearest_points(countyGeom[closestCounty],countyGeom[c])\n",
    "            for t in countyTractList[c]:\n",
    "                islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "            for t in countyTractList[c]:\n",
    "                plotPoly(tractGeom[t])\n",
    "                plotCenter(t,tractGeom[t])\n",
    "                plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                tractCP[t] = tractGeom[t].centroid\n",
    "                tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                plotPoly(tractGeom[t],0.2)\n",
    "        else: #move only the county's island tracts to connect with main geom\n",
    "            mainCountyGeom = countyGeom[c].intersection(mainGeom)\n",
    "            plotPoly(mainCountyGeom)\n",
    "            plotCenter(c,mainCountyGeom)\n",
    "            for t in countyTractList[c]:\n",
    "                if tractGeom[t].intersects(nonMainGeom) and t not in skipList:\n",
    "                    if tractGeom[t].intersects(mainCountyGeom):\n",
    "                        print(\"unit\",t,\"has island pieces.  We will reduce the tract to its main state component\")\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "                        tractGeom[t] = tractGeom[t].intersection(mainCountyGeom)\n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                    else:    \n",
    "                        isIsland[t] = True\n",
    "                        print(\"unit\",t,\"is a true island.  We will move it to adjoin the mainland in same county.\")\n",
    "                        islandList.append(t)\n",
    "                        if tractGeom[t].geom_type != dummyPoly.geom_type :  #island tract is multiple geoms.  collapse to its largest isle\n",
    "                            isleGeos = [geo for geo in tractGeom[t].geoms]\n",
    "                            isleAreas = [geo.area for geo in isleGeos]\n",
    "                            biggestIsleNo = isleAreas.index(np.max(isleAreas))\n",
    "                            tractGeom[t] = isleGeos[biggestIsleNo]\n",
    "                            tractCP[t] = tractGeom[t].centroid\n",
    "                        p1, p2 = nearest_points(mainCountyGeom, tractGeom[t])\n",
    "                        islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                        plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                        tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a94118d-5863-4458-b77a-67d18cf01c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\n",
      "This would need adjustment for multi-county islands like Michigan UP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with island triage - RUN ONLY WITH ISLANDS\n",
    "print(\"If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\")\n",
    "print(\"This would need adjustment for multi-county islands like Michigan UP\")\n",
    "for c in range(nCounties):\n",
    "    if hasIsland[c] :  #rebuild the county geom with the translated islands\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in countyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "MAP = countyGeom[0]\n",
    "plotPoly(countyGeom[0])\n",
    "for c in range(1,nCounties):\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "    plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block reads in a csv with whole districts to be cut out of the map\n",
      "   For this version, we ignore the nDistricts parameter in the cutPoly file\n",
      "For OH my input file says to cut out 0 districts out of 99\n",
      "There were no cut districts in OH\n"
     ]
    }
   ],
   "source": [
    "print(\"This code block reads in a csv with whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "#nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "print(\"   For this version, we ignore the nDistricts parameter in the cutPoly file\")\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "                            if STATE == \"NC\":\n",
    "                                if t == 1828:\n",
    "                                    print(\"special for NC - include vtd 1828 from county 55 in the cut list for contiguity\")\n",
    "                                    cutList.append(t)\n",
    "                                    cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts) )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "if nCutDistricts == 0:\n",
    "    print(\"There were no cut districts in\",STATE)\n",
    "else:\n",
    "    print(\"here are your cut districts\")\n",
    "    plotPoly(MAP)\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        cutGeo = tractGeom[L[0]]\n",
    "        for v in L:\n",
    "            cutGeo=cutGeo.union(tractGeom[v])\n",
    "        plotPoly(cutGeo,2)\n",
    "        plotCenter(i,cutGeo)\n",
    "        plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")\n",
    "cutDistrictNo = list()\n",
    "for v in allCutVTDs:\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        if v in L:\n",
    "            cutDistrictNo.append(i)\n",
    "            break\n",
    "nbrDF = pd.DataFrame( {\"vtdNo\":allCutVTDs,\"cutDistrictNo\":cutDistrictNo} )\n",
    "outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99 districts\n"
     ]
    }
   ],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]\n",
    "print(nDistricts,\"districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before squishing in outcroppings.\n",
      "Of the total statePop 11799448.0 we ignore 0.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 7 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 99 rather than original 99\n",
      "Each district will be drawn to house 119186.343 = 1/ 99 of non-cutout state pop 11799448.0 vs original= 11799448.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "working on county 20\n",
      "working on county 40\n",
      "working on county 60\n",
      "working on county 80\n",
      "here is the OH map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before squishing in outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "        for t in countyTractList[c]:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 11\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 7\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "Working on county  20 distances\n",
      "Working on county  40 distances\n",
      "Working on county  60 distances\n",
      "Working on county  80 distances\n",
      "the max LAT-scaled distance between counties is 3.86947\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 3.86947  2.2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  0 neighbors\n",
      "Working on county  20 neighbors\n",
      "Working on county  40 neighbors\n",
      "Working on county  60 neighbors\n",
      "Working on county  80 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For corners and outcroppings, we fuse county clusters\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f9b3df93-d821-44ca-b4f4-b65d13baaffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing from above, develop corner county blocks from each low-pop corner county until it hits 0.25 of 119186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below I plot these again by county no, with faded neighboring counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are 119186.343 29796\n",
      "You may suggest [ [3], [43] , [85] ] \n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if you want to manually add another corner-county cluster 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our final pops and fused-county lists are...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aDP = float(statePop)/nDistricts\n",
    "maxCCBratio = 1./4.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "maxCCBpop = maxCCBratio * aDP\n",
    "print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "nFuses = 0\n",
    "for c in has1neighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "    if countyPop[c] > maxCCBpop:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        nc = neighborCountyList[c][0]\n",
    "        plotPoly(countyGeom[nc],0.5)\n",
    "        plotPoly(MAP,0.2)\n",
    "        print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "        print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "        fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "        if fuseChoice == 0:\n",
    "            keepUnfused = True\n",
    "            break\n",
    "        elif int(fuseChoice) == 1:\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])  \n",
    "            hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "        elif int(fuseChoice) == 2:\n",
    "            CCBlist.append([c,nc ] )\n",
    "            CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "            hasFewNeighborCs.append(c)\n",
    "            passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "    else:\n",
    "        hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "        \n",
    "for c in hasFewNeighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "    if countyPop[c] < maxCCBpop :\n",
    "        CCBlist.append([c])\n",
    "        CCBpop.append(countyPop[c])\n",
    "        CCBno = CCBlist.index([c])\n",
    "    if c in passThruList:\n",
    "        CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "    if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "        CCBstarter = c\n",
    "        canAdd = True\n",
    "        while canAdd:\n",
    "            nbrSet = set()\n",
    "            for cc in CCBlist[CCBno]:\n",
    "                nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "            nPop = [countyPop[cc] for cc in nbrSet]\n",
    "            nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "            idx = np.argsort(nDist)\n",
    "            nbrList, newList = list(nbrSet), list()\n",
    "            for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                cc = nbrList[idx[i]]\n",
    "                if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                    newList.append(cc)\n",
    "                    CCBlist[CCBno].append(cc)\n",
    "                    CCBpop[CCBno] += countyPop[cc]\n",
    "                    #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "            if newList == list():  #no eligible neighbor counties found; stop\n",
    "                canAdd = False\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    plotPoly(countyGeom[cc])\n",
    "                    plotCenter(CCBno,countyGeom[cc])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "                    \n",
    "print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "plotPoly(MAP,0.15)\n",
    "for L in CCBlist:\n",
    "    print(CCBlist.index(L),\" \",L)\n",
    "    for c in L:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "            plotPoly(countyGeom[cc],0.4)\n",
    "            plotCenter(cc,countyGeom[cc],8)\n",
    "plt.show()\n",
    "rejectList = list()\n",
    "print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "print(\"You may suggest [ [3], [43] , [85] ] \" )\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "        print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "        isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "        if not (isOK == 1 or isOK == str(1)) :\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                rejectList.append(L)\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "                print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                CCBlist[i] = Lnew.copy()\n",
    "for L in rejectList:\n",
    "    del CCBpop[ CCBlist.index(L)]\n",
    "    del CCBlist[CCBlist.index(L)]\n",
    "stillAdding = True\n",
    "while stillAdding:\n",
    "    addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "    if int(addMore) != 1:\n",
    "        stillAdding = False\n",
    "    else:\n",
    "        isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "        if isOK == 0 or isOK == str(0) :\n",
    "            print(\"OK, we'll stop adding corner clusters\")\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            notGood = True\n",
    "            while notGood:       \n",
    "                Lnew = ast.literal_eval(isOK)        \n",
    "                if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                    notGood = False\n",
    "                    for L in CCBlist:\n",
    "                        if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                            notGood = True\n",
    "                            print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    if notGood == False:\n",
    "                        CCBlist.append(Lnew)\n",
    "                        CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                else:\n",
    "                    print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                    isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 2383\n",
      "We defined a total of 0 unit (but not corner-cluster) counties in the map, excluding the cut-out districts\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties > 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "origMAP = MAP\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    MAPexterior = MAP.exterior\n",
    "else:\n",
    "    maxArea = 0\n",
    "    for geo in MAP.geoms:\n",
    "        if geo.area > maxArea:\n",
    "            MAPexterior = geo.exterior\n",
    "            maxArea = geo.area\n",
    "            MAP0 = geo\n",
    "    MAP = MAP0\n",
    "for c in uncutCountyList:\n",
    "    if countyGeom[c].intersects(MAPexterior):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop and c not in allFusedCounties:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map, excluding the cut-out districts\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented VTDs?\n",
      "720 fragmented VTDs out of 8941\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI, here are the locations of fragmented VTDs with pop > 3000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented VTDs?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 3000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 3000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec79d0b6-a118-4778-b650-2a351a6fe0c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets visualize the Marblehead peninsula, so we can collapse to Danbury and connect to Bayview\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Lets visualize the Marblehead peninsula, so we can collapse to Danbury and connect to Bayview\")\n",
    "for c in [21,61]: #[21, 61] [21]\n",
    "    for v in countyTractList[c]:\n",
    "        if tractCPx[v]< -82.5 and tractCPx[v] > -83.1 and tractCPy[v] > 41.41:\n",
    "            plotPoly(tractGeom[v])\n",
    "            plotCenter(v,tractGeom[v],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e71c79b2-de51-41c0-8b1c-534d479ab6e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Identifying the big coastal tract that defines the Sandusky-area border\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Identifying the big coastal tract that defines the Sandusky-area border\")\n",
    "plotPoly(tractGeom[3730])\n",
    "plotCenter(3730,tractGeom[3730])\n",
    "plotPoly(tractGeom[3622])\n",
    "plotPoly(tractGeom[933])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0240e244-4cfe-4fee-8336-1a7af28f4d21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sdedLists = [ [1006, 925, 926, 927, 928, 935, 937, 986, 988], [3739] ] \n",
    "sders = [933, 3622]\n",
    "for i,v in enumerate(sders):\n",
    "    plotPoly(tractGeom[v])\n",
    "    for vv in sdedLists[i]:\n",
    "        plotPoly(tractGeom[vv])\n",
    "        plotCenter(vv,tractGeom[vv])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ff6c40a8-9a10-4cd5-b26e-af327cec1abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for v in countyTractList[72]:\n",
    "    if tractCPx[v] < -82.9 and tractCPx[v] > -83.1 and tractCPy[v] < 38.8:\n",
    "        plotPoly(tractGeom[v])\n",
    "        plotCenter(v,tractGeom[v])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\n",
      "I split up 720 fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\n",
      "looking for surrounds, now working on vtd 0\n",
      "looking for surrounds, now working on vtd 500\n",
      "looking for surrounds, now working on vtd 1001\n",
      "looking for surrounds, now working on vtd 1502\n",
      "looking for surrounds, now working on vtd 2002\n",
      "looking for surrounds, now working on vtd 2502\n",
      "looking for surrounds, now working on vtd 3003\n",
      "looking for surrounds, now working on vtd 3503\n",
      "WARNING. vtd frag 3739 in county 21 intersected county 61 but had no intracounty neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for surrounds, now working on vtd 4004\n",
      "looking for surrounds, now working on vtd 4505\n",
      "looking for surrounds, now working on vtd 5006\n",
      "looking for surrounds, now working on vtd 5506\n",
      "looking for surrounds, now working on vtd 6006\n",
      "looking for surrounds, now working on vtd 6506\n",
      "looking for surrounds, now working on vtd 7006\n",
      "looking for surrounds, now working on vtd 7506\n",
      "looking for surrounds, now working on vtd 8006\n",
      "looking for surrounds, now working on vtd 8507\n",
      "WARNING.  We did not find another neighbor of surrounder 988 other than surroundee 3739\n",
      "On loop 2 for county 41  we found that vtd frag 98 now has only 1 neighbor 8962\n",
      "On loop 2 for county 86  we found that vtd frag 189 now has only 1 neighbor 8996\n",
      "WARNING.  We did not find another neighbor of surrounder 3739 other than surroundee 988\n",
      "On loop 2 for county 61  we found that vtd frag 988 now has only 1 neighbor 3739\n",
      "On loop 2 for county 51  we found that vtd frag 1094 now has only 1 neighbor 9264\n",
      "On loop 2 for county 15  we found that vtd frag 1922 now has only 1 neighbor 9345\n",
      "On loop 2 for county 20  we found that vtd frag 5334 now has only 1 neighbor 9824\n",
      "On loop 2 for county 68  we found that vtd frag 10052 now has only 1 neighbor 10055\n",
      "On loop 2 for county 31  we found that vtd frag 7621 now has only 1 neighbor 10564\n",
      "special for OH , I believe that [334, 343] are surrounded by vtd (fragment) 312 . Here, let me show you\n"
     ]
    },
    {
     "data": {
      "image/png": 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oIQDjeafAIozn9foYvr/fUk8QB0TThRhYvMBZQkQUCs4PQLHm9rd3fgCqd3qaen5qz9632t0oqbGfDT2epm11jtYDkEGnOS/gXNwTFG7QNdt+LiwZdAxAgY6BxQsMLEREvmsWgDqoTYfb02zcT2PIabxvs7tRWmNH3Xnh6Nxz3bJ80TpCsgB+O6YX+nWN6qDqyJ8YWLzAQbdEROpg1Glh1GkRE+HbNPiWvPrFEdjs7g6oijoD+8i8wEG3RETBp8zmQEKkgtcrIZ8wsHiBg26JiILPaasdCZYwpcsgLzGweEGrkZotEU5ERIGv3unxy8KD5B8MLF5gDwsREZGyGFi8wGsJEREFH17sILAwsHiB1xIiIgoutQ43Ing6KKAwsHhBK0nwcJYQEVHQOG21c4ZQgGFg8QIH3RIRBZcyqwOJnCEUUBhYvMBBt0REwaXMZkeChT0sgYSBxQvsYSEiCi5lVgfieUoooDCweIE9LEREwaXUakdylEnpMsgHDCxe4KBbIqLgUm5jD0ugYWDxAk8JEREFl3qnGxEGrdJlkA8YWLzAU0JERMFHkrh0XCBhYPGCRgIETwkREQUN/koPPAwsXmAKJyIiUhYDi5eYxomIgoPd5YFBx4+/QMN/MSIiCinV9S50iTAoXQb5iIHFSzwrREQUHKrqnegSrle6DPIRAwsREYWUxsDCHpZAw8BCREQhparOxcASgBhYiIgopFTVO9ElgqeEAg0DCxERhZSqOp4SCkQMLEREFFKq6nlKKBAxsBARUUjhoNvAxMBCREQhxWZ3ITJMp3QZ5CMGFiIiCilCABoNF9cKNAwsREREpHoMLERERKR6PgWWBQsWICcnBxaLBRaLBbm5uVi9enXT9tLSUkyZMgVJSUmIiIjA4MGDsWzZsku2abPZ8Jvf/Abdu3eHyWRCXl4etm7denl7Q0REdAkujwwtTwcFJJ8CS0pKCubNm4f8/Hxs27YNN954IyZMmIC9e/cCAKZOnYoDBw5g5cqVKCgowK233opJkyZhx44drbZ57733Yu3atfj3v/+NgoICjBkzBqNGjUJRUVH79oyIiOgC1ZzSHLB8Cizjx4/HuHHjkJWVhV69emHOnDkwm8345ptvAACbNm3CAw88gOHDh6Nnz554/PHHER0djfz8/Bbba2howLJly/DnP/8Z1157LTIzM/F///d/yMzMxIIFC9q/d0REROepqncimqvcBqTLHsPi8XiwZMkS1NXVITc3FwCQl5eH9957D5WVlZBlGUuWLIHdbsf111/fYhtutxsejwdhYWHNHjeZTNi4ceMl39/hcMBqtTa7ERERXUpVnRMx7GEJSD4HloKCApjNZhiNRsyYMQPLly9HdnY2AGDp0qVwuVyIjY2F0WjEfffdh+XLlyMzM7PFtiIjI5Gbm4tnnnkGxcXF8Hg8eOedd7B582aUlJRcso65c+ciKiqq6ZaamurrrhARUYjhonGBy+fA0rt3b+zcuRNbtmzBzJkzMW3aNOzbtw8A8MQTT6C6uhrr1q3Dtm3b8PDDD2PSpEkoKChotb1///vfEEKgW7duMBqN+Mc//oHJkydDo7l0abNnz0ZNTU3TrbCw0NddISKiEFNV70KXCAaWQCQJIUR7Ghg1ahQyMjLw6KOPIjMzE3v27EG/fv2abc/MzMQrr7xyyXbq6upgtVqRnJyMO+64A7W1tfj444+9rsNqtSIqKgo1NTWwWCyXvT+tuXfRVvxr2rAOb5eIiDrPy58fxlU9YzCke4zSpdBZ3n5+t3sdFlmW4XA4UF9f39jgBT0jWq0Wsiy32U5ERASSk5NRVVWFNWvWYMKECe0tjYiIqBleqTlw+XQxhdmzZ2Ps2LFIS0uDzWbD4sWLsWHDBqxZswZ9+vRBZmYm7rvvPjz//POIjY3FihUrsHbtWqxataqpjZEjR2LixIm4//77AQBr1qyBEAK9e/fG4cOH8bvf/Q59+vTB9OnTO3ZP202CLAsu50xEFMAq652I4SmhgORTYCkrK8PUqVNRUlKCqKgo5OTkYM2aNRg9ejQA4JNPPsGsWbMwfvx41NbWIjMzE4sWLcK4ceOa2jhy5AgqKiqa7tfU1GD27Nk4deoUYmJicNttt2HOnDnQ69U17UwjAbIQ0ICBhYgoUFkb3LCEqevzhbzjU2BZuHDhJbdnZWW1ubLt8ePHm92fNGkSJk2a5EsZipAkoF2DfYiISHFCsKc8UPFaQl6SIKF9w5OJiIjocjGweEmjAQT7WIiIiBTBwOIl9rAQEQU2XvgwsDGweEsCAwsRUQArrbEj0RLW9hNJlRhYvCSBp4SIiALZ/hIr+iZ3/MKi1DkYWLwkSTwlREQUyPaVWJHdlYElUDGweKmxh4WIiALVgVIbeidGKl0GXSYGFi9ppMb5+0REFJjsLg9MBq3SZdBlYmDxkiRJkJlXiIgCUk2DC5Fc4TagMbB4SQJ4ToiIKEDt5/iVgMfA4i2Js4SIiALVvmIrsjlDKKAxsHiJC8cREQWuvcXsYQl0DCxe0vDih0REAaui1oE4s1HpMqgdGFi8JHGWEBFRQKqsc6JLOAfcBjoGFi9J4CwhIqJAtONkFQZ376J0GdRODCxekjjologoIOWfqMLgNAaWQMfA4iWJS90SEQWkeqcHkWE6pcugdmJg8ZIkScwrREQBKM5swJk6p9JlUDsxsHhJAjitmYgoAMWajThTy8AS6BhYvMQxLEREgSk2woAztQ6ly6B2YmDxEmcJEREFplieEgoKDCxe4josRESBKTaCp4SCAQOLlzQSl+YnIgpEjT0sPCUU6BhYiIgoqJmNOtTa3UqXQe3EwOKlxlNCSldBRES+kiRJ6RKoAzCweKlx0C0TCxERkRK49J+XJF6tmYgooK0uKEG4UQezUYtwgw5mow4RRh3CDVoYdRr2xKgcA4uXGheOY2QhIgpEv7upN/YUWVFmc6DW4Ua90406h6fpe6dbbnquEGcvx3Le93qtBpYwPaLC9Ygy6RF99muEsTH4nLtFGHWIMGph1GkV2tPgxcDiJY2GS/MTEQWqPkkW9EmyXPbrHW4PrA1uWO0uVNc7UV3vQk2DC1V19ah1eFDrcDUFoFq7Gy6P3GI7YQYtzIbGYGMOa+ztibgg8Fz4fZievT8AA4vXuDQ/EVHoMuq0iI/UIj7SeNltCCFgd8mNocbhRt0FX0ut9sbv7W7UOjxNjzvcnos+fwQAo06DCKMOkWE6RIbpYQnTwWLSwxKmh8Wka/x69vvIMD20msAOPQws3pIAjmIhIqLLJUkSTAYtTIb2BZ9zHG4P6hwe2Owu2OxuWBtcsNpdsDa4cKqqHtbzHrPZ3ZBlcdGprguDTmRLocfU+Lheq+w8HQYWL3FpfiIiUhOjrnGsTEyE4bJe75EFah2NocZmdzeFHavdjdIae7PHDpbV4ufXpOPmnK4dvBfeY2DxEtdhISKiYKLVSIgyNQ4ebsuiTcdhNiobGbgOi5c0vFozERGFqFKrHUlRYYrWwMDiJQm8lhAREYWm0zV2JFkYWAICTwkREVGoqmlweXXqyJ8YWLwkgaeEiIgodCm9FgwDi7cknhIiIqLQI4Q6/lxnYPGShqeEiIgoBNkcbkSGKT+pmIHFSxIknhIiIqKQo4YBtwADi9c46JaIiEKRGqY0AwwsXmscdEtERBRaTlsdSGQPS+CQJEBmFwsREYWYcpujQ6591F4MLF6SOEuIiIhCULnNgXgzA0vAkHi1ZiIiCkEVtQ7EsYclcHBpfiIiCkV1DjciDFqly2Bg8ZYksX+FiIhCk9Kr3AIMLF6TwGnNRERESmFg8RJnCRERUahxumXotMr3rgAMLF7TcJYQERGFmDN1DsSpYIYQwMDiEy7NT0REoaTC5mRgCTQSR90SEVGIKa+1q2LROICBxWtcmp+IiEINe1gCEAfdEhFRqCmvVcey/AADi9c46JaIiEKNWpblBxhYvMYhLEREFGrKax2IizQoXQYABhavNS4cx8hCREShw+HyINygU7oMAAws3pMk9rAQEVFIUdPf6QwsXuLFmomIiJTDwOIljSRxlhAREYUMu8sDo149MUE9laicJKmra4yIiMifKmrVsyw/wMDiNS4cR0REoaSiVj2LxgEMLF5r7GFhZCEiotBQblPPonEAA4vXJHCWEBERhY6APiW0YMEC5OTkwGKxwGKxIDc3F6tXr27aXlpaiilTpiApKQkREREYPHgwli1bdsk2PR4PnnjiCaSnp8NkMiEjIwPPPPOM6nozOIaFiIhCSbnNgTizOhaNAwCfVoNJSUnBvHnzkJWVBSEEFi1ahAkTJmDHjh3o168fpk6diurqaqxcuRJxcXFYvHgxJk2ahG3btmHQoEEttvmnP/0JCxYswKJFi9CvXz9s27YN06dPR1RUFB588MEO2cmOIEmS6kIUERGRv5wJ5B6W8ePHY9y4ccjKykKvXr0wZ84cmM1mfPPNNwCATZs24YEHHsDw4cPRs2dPPP7444iOjkZ+fn6rbW7atAkTJkzAD3/4Q/To0QM//vGPMWbMGHz77bft27MOxkG3REQUSs7UORGroh6Wyx7D4vF4sGTJEtTV1SE3NxcAkJeXh/feew+VlZWQZRlLliyB3W7H9ddf32o7eXl5WL9+PQ4ePAgA2LVrFzZu3IixY8de8v0dDgesVmuzmz/xlBAREYUSu4qW5Qd8PCUEAAUFBcjNzYXdbofZbMby5cuRnZ0NAFi6dCnuuOMOxMbGQqfTITw8HMuXL0dmZmar7c2aNQtWqxV9+vSBVquFx+PBnDlzcOedd16yjrlz5+Lpp5/2tfzL1njxQyYWIiIKDWr7I93nHpbevXtj586d2LJlC2bOnIlp06Zh3759AIAnnngC1dXVWLduHbZt24aHH34YkyZNQkFBQavtLV26FO+++y4WL16M7du3Y9GiRXj++eexaNGiS9Yxe/Zs1NTUNN0KCwt93RWfSJBU949HRETkD2ocsymJdlY1atQoZGRk4NFHH0VmZib27NmDfv36NduemZmJV155pcXXp6amYtasWfjVr37V9Nizzz6Ld955B999953XdVitVkRFRaGmpgYWi+Xyd6gVH+4sAgBMGNitw9smIiJSk5p6F57+aC/+dsdAv7+Xt5/f7V6HRZZlOBwO1NfXNzaoad6kVquFLMutvr6+vt7n1yhBkiSlSyAiIuoURdUN6BptUrqMZnwawzJ79myMHTsWaWlpsNlsWLx4MTZs2IA1a9agT58+yMzMxH333Yfnn38esbGxWLFiBdauXYtVq1Y1tTFy5EhMnDgR999/P4DGmUdz5sxBWloa+vXrhx07duBvf/sb7r777o7d03aSAMjq6yEjIiLqcEXVDejWJYADS1lZGaZOnYqSkhJERUUhJycHa9aswejRowEAn3zyCWbNmoXx48ejtrYWmZmZWLRoEcaNG9fUxpEjR1BRUdF0/8UXX8QTTzyBX/7ylygrK0PXrl1x33334cknn+ygXewYHHRLREShori6AT3iIpQuo5l2j2FRC3+PYfloVzHcsoyJg1I6vG0iIiI1ee6T/Zg0NBWZCWa/v1enjWEJFbIQ0HAcCxERhYDGMSxhSpfRDAMLERERNWN3qmvROICBhYiIiAIAAwsRERE1cbg90GvVFw/UV5FKcR0WIiIKBaU1diSrbPwKwMBCRERE5ymqakA3lS0aBzCwEBER0XmKqhlYiIiISOXUuMotwMDiNQnqu9Q2ERFRRytW4XWEAAYWIiIiOk9lnROxEQaly7gIAwsRERE1EUKdM2MZWIiIiAgAIMsCKswqAHy8WnMo49WaiQKDx+PBd8ePIjKi8UqzJ4tL4HZ7YDDoodfpoNdpodPpoNc33jfodTDoDDDo9Y2PaXXQabXQaPj3HIWeM3VOxEYYlS6jRQwsRBRUCg4dgLW2Dg12BwCgV3oPGHR6OJxOON0uuFxuON0uOF1O1NXXw+32wOV2w+32wO12wyPL8HjkVts/98eny+XG+JE3dMIeEXUetc4QAhhYiCjIlJadwegRedBqtX59n0+++NKv7RMpoahKnTOEAI5hIaIgM2n8zTCbzTCZTIiKisJ//vMfAMCAAQOg0+kgSRLee++9pudXV1cjOTkZBoMBJpMJsbGxWL9+vVLlEymqWKWLxgEMLF6TIHEdFqIA8I/XXkdDQwMaGhowY8YM3H333QCAe++9F5s3b26x52XGjBmw2+1oaGjAjTfeiNtuu62zyyZSBbWucgswsBBRkElMTm76/syZM03fP/DAAxg2bNhFz4+OjsZTTz3VNMh2/PjxqK2t9X+hRCpUbnMgwaLOQbcMLEQUsBYsWICcnBxYLBZYLBbk5uZi6zebkZGRAZ1Oh7feegvvvPMOhBAYO3YsJEmCOK+r9MyZM7jpppvQtWtXGI1GpKam4v7770ffvn0V3Csi5TjcHhh16owG6qxKhdQ6L50olKWkpGDevHnIz8/Htm3bcPU1V+OZP/wBK1euhNvtxvTp0zFz5ky88MILLS6EpdFoMGHCBKxcuRIHDx5EYmIiamtrkZWVpcDeEKmDGheNAxhYfMIxLETqMn78eIwbNw5ZWVno1asX7rx7OiIiIvDNN98AAF5//XWUl5fjT3/6E954442LXh/p8WDKFf2RVVKCX/3qV9i/fz+efPJJbN26tbN3hYjawGnNRBQUPB4PXv3nq6ivr0dubi4A4JFHHgHQeOpI8+naxicKgbKl72Pjvz4E6hrXannsu2+wo6ES6z/7DLNnz8Z1113X5vup9a9QosvxxcFyLN1WCLes3r/MGViIKKA9/vjjmDt3LmS5cbE3SZIwcOBAaLVayLKM5ORkPPLIIzh69CgAQBYCv/5gGbQaDc58shpbNn2NTZs/AADk5eU1tfvSSy8hOjq61fcV7HKlIPLRrmL8YVxf1a7BAjCweI1/SxGpQ7XNig2bt8Jg1Dc+YDTiVw//FlHRUdjy9SZs3PA5XC4X/vKXv2D+/Pl44IEHMHDgQNS9+ibGLX4LQ3JycLq8HMcLC6HVajHc6cSKwWOR9a85mD5jBvbt28dZQhRyquqcSI4KU7qMS2Jg8QH/niJSniwLxMRYcO3QxinK4667ttn2UaNG4auvvsKHH36II0eO4OGHHz73QgDAjj17IMsyjh8/joyMDMh1tYgJM2HD5s0wmUx49tln8Zvf/AalpaWX7GEhCjZqP83JQbdEFFCiIyNRV29vcZvH40FJSQncbjcef/xx7N69Gzt37sS3H32EhTkjAQDXXHMNUlJSkJqaCgCQ7XYcd9ThmWeewdtvv93UVkNFBSr+/Q6qP1ndrnrdHhm1Djdq6l2QVTw+gEjt2MNCRAHl/Ksoz549G2PHjkV9fT1uvfVW2O12CCHw7LPP4rrrrsOpF/6Bht0F+GDfHswr2AYAOHToED7+3QzUzJ6K9aU1OPztQbx66jTuv+UH2PL2Ivx57jwAQNHvXoBNo4EkZFw9aiQkg6FZHa39Ner2yPj2WCU+3XcaR8prYdJrEabXQqeRYLU3XlzRYtIjt2csbuybgIRIdXfDU/BrcHoQpvfvtbc6AgOLl1TeU0YUksrKyjB16lSUlJTAbDajf//+SE9PxwsvvICJEyfC9u4nONXtWgyTy7D+uTnIm/UY4mqrceesOVjSOxUOqwP/KCxGlSzjj++vQMLKT9E7MgnF9UcRV1GAKK0GFbF9IWpKIcWnNXvvc4NuZVlg05EzWLmrCOU2ByRJwpDuXfCzq7ojIz6ixWBTWefE14cr8PTKfbiiWxRmXp/RKceLqCXlNgfiI9W5uu35GFiIKOCcCwsLFy5scfuoG27As1On4T5TFnqcWIMr3nsdtkXzsL9vb4hwGUMLjmPPo09h2k/vxN9zrsCZfd9BAChx1KHYcQwAcPXRw3js9lG4Or0WGkcZgDTUOdzYeeI0Nu3eh5OllVhdvAOVdQ7k9ozFAzdmITUm3Kv6YyIMGD+gKzLizXjliyMdcUiILluZzc7AEmw4jZFI/eo2b0bV7n3Q68OR/dxMhEe7cGraRDirPYi9Ph2RT/wTIrMfHC43IElYtmIlGhoaml6/detW3H333fjyP39DQt132HWsGM+9vx5HxEEYdBISoyIwoG8m7hl7HcL0GoTptNBoLq8LNjkqDAadBg+/txO3DOyK2AgjkqPDEGdW/4cHBY9ymwMJDCzBhOeEiNTi/NMs58axJELCoX+9gf98vgU7Ksvw4XNP4uDSl/Df/FO4sUcUej/zGA53zca8Gb+GyWTCuHHjAAAZGWdPxzhq4TrwPxzc928AQPb2J6Azx8DQbThGjLsNSamZHb4fXSIMeP72ASisrMeavaXYVl+F/BNV+POPc7zuraHg55EFymx2WML0CDdoO3w2T5nNge6x6v95Y2AhooBWcvgwJv/wZpTX1SFCp0dOZjre+9EIZL3zHio0buxNsGDxSSuqfvpLJCYm4tprr8WmTZuQkJAA2Gvg3L8aVVvfR0zJl9ALJ7S13Robvnc9zBlDYe6EAWypMeG495qeAICi6gY8snQX5ky8Aj3jzX5/b1K/b46ewQvrDqJrtAl1Ds/ZR7/v8ddrNYgy6WEx6WEJ0539qofFpGt8PEzf9FiYXnNR4Cm3OTCsR0wn7tHlYWAhItX6YutW1DdcPIW58kxN0/e/0lrwi6vHo++08RBHN6Li/XWQD1QgZvQVyPq/f+LTLgnNX1xfCcfeVTj9yqOIPf01DMIFTVR/2K+ZDf2gWzGhSw+IBf7es9Z1izZh/k8G4jfv7cSCnw1BlEmvXDGkCqU1dkzL64Gbc7q2uN3h9sBmd6OmwQVrgwtWuxvWBhdKauz4rtQGa4MbVnvjNrvL03RdPElqvEbe4fJaTB/Ro/N26DIxsPiAI1iIOlddfQNuuubqZlOZz+cqKYH9ZA16XmFE1T+egaNShmVgAhKe+hv0fYd+/0QhIIryUbVpEaL2vQs9ZEhdBsF+/VMwD5yI+KiUTtoj7yRYwvDoTX0w+4PdeGny4MseI0PB4bTNfskeEKNOC6NZe1ljn4QQcHkEDDr1L8vGwOIlTmsm6nyWyAhUVFchISa2xe2FM+4GEAHrl9sRnhqGHs89DtP1t33/BLcD9dvehfOLvyO64SSMui6oHf4goq6ZgYTIpM7Zics0MDUaIzLj8K+NR/GLazntOZSVWf03KFaSJBh0gfEBx8BCRKqVFBeHkvKyVgOLp6Icbl08ksaFI/r5rZDO9cTIMhry34Vr7TMwO8tQmzIG8g0vIqLH1YA2cH7t/XR4Gqa/tRW3Dk7hzKEQ1jiLhwsMBs7/XCIKOZERESirrAQAFFeUIX/XXjhdbkRHmzHyqlxkfb0NyatmY83WGtx29DOgxzVoyF8M+4YX0KXhOGrTxsIy/mkkxPdWeE8ujyRJmHFdBhZtOo7fjgnMfaD2s7s8MBnUvxKtvzGw+IKDWIg6VbgpDFXVVqzZuBHV1bW4fdwYaDQafLT+c8iyDI1GA/O4ZzGsbCrW/Ps5XKf7DkZPPWzdRkL+wb8QnzZM6V1ot+E9YvDy54eVLoNIcQwsXgqMM3xEwSXcaILRoEfu4EGICDM1Pd6vdyby9+3FsCv6w+rw4E1pIlJ1H8PWMwPxY36HhPheClbdsTQaCT1iI3C0vJbTnEMQFyz9HgMLEamWVqvFqLy8ix7vmZKK3d8dwhcHP8P+o6dw87VD8aO7buvwBbXUYmTfBHz2XRkDSwiyOdyIDONHNQCofx6TigieEyJSnBACq3YX47/7amCOCMdrD0/BxOHZQRtWAGB4egy2Hq9UugxSQJnVjgQLB9wC7GHxWjD/MiQKFJuOVGDBhiO4JisO/5p5C8L0oTEQMdygg9MtQ5YF12QJMf6c0hxoGFiISPX2l1jxwrqD6BEXgZcmD0ZUeOit/pocbUJxTQNSuqj/mi/UccpsjoC4knJnYGDxAcc+EXUuh9uDZ1fth9Mt46nx/dA12tT2i4LUtVlx+PxAOaZc1V3pUqgTldnsyEmJVroMVWBgISJV2n2qGn9ZcwB35fXAyL6JSpejuDC9FrLMv5pCzWmeEmrCwOIlnjWmUHPuImmdvWCVLAs898l+2Oxu/G3SQHaHn9Ul3IBdhTVtP5GCSpnNwUG3ZzGw+IB/21CwOH/wphAC35XasPtUNU5bHdhy7AxMei0MusbL0DvOBhcBwKDVoFsXE1K6mJDaJRwpMSakx0XAqOuYUHOkvBbzVn+Hm/ol4bYh6rogodISLEaU2S6+cjUFt3qHG2YjP6oBBhaikHCm1oFvj1Viy7FKHK2ogxACJr0W5jAdKmqd6JMUicFp0YgzGzHz+gzotS2veGB3eVBc3YBTVQ0orKrHthNVOFJeC48s0CcpEsPSYzA4rQuiTN4NipVlgU/3ncaMd/KRZAnD8PQYPPHDbKTFcmDphWIjjKiodShdBpFiGFi8xFnNFGhkWWD1nlK8t60QsREGDE+PwdTc7kiPi4AkSah1uFHvdPt0UbUwvRY9480XLWDm9sj4rtSGbccr8cH2IlgbXMiINyMjIQLxZiPiIo2NX81GVNU7sb/EijV7S1FqdeDqzFhc1TMGs8f2xYDU6A4+CsHjTJ2DF0CkkMbA4gPOEqJAUVpjx6PLduPqzFi8NmVIi+uVmI26Dutq1mk1uKJbFK7oFoW7RjSeZjpcVouTlfUotzlw8LQN5TYHKmqdiArXo3diJB4cmdU0RfcX12Z0SB3B7EhZHVe6DTF1DjcvengeBhaiIFNU3YBHlu7C3Fv7o0dchCI1SJKErMRIZCVGKvL+wehIeS0y4pX59yRllNkcSOSA2yZcmt8HXJqf1M7u8uDR/+7Cn3+co1hYIf9oDCzsYQklp612Tmk+DwOLlziGhQLBXz89gOl56UiN4aDVYFNU1YBuIbxwXihqnNLMwHIOAwtRkPj8QBkaXB6MyuYia8FGCAGP4HWEQk2Z1Y5EHwbFBzsGFh9w0C2pVXF1A97YeAyP/zBb6VLIDw6V1SKTp4NCDntYmmNg8ZIQPC1E6iTLAk+s2IM/TrgiZK5eHGq+OlSBq7PilC6DOlmZ1Y549rA0YWDxkhCAxAX6SYX+t7cUw9NjkM5BtkFr67FKDE+PUboM6mQ2uxuWME7mPYeBxUsC7GEh9fHIAu9uOYGpuT2ULoX8xOWR4fLICDfwgysUSfzgacLA4iUhBPtXSHU+LijByD6JXFwqiO0srMZArgBMxMDiLfawkNp4ZIEl357ET69MU7oU8qNtx6swjKeDQo7d5YFRz4/o8/FoeIljWEhtVu4qwk1XJHGgbZDbfaoaOSlRSpdBnazM6vDpOl+hgIHFa4I9LKQabo+M97edwh3DUpUuhfxICIEGl4fjV0LQaZudU5ovwMDiJVlw8BOpx/IdRRjXPxlGHXtXgtkprm4bssqsDi4adwEGFi81nhIiUl5NvQsfbC/CpKHsXQl23xw9gyt7xipdBingtNXOCx9egIHFS4KnhEgFnG4ZDy/did+P6wuDjv99g92WY5W4igNuQxJXub2YT7/xFixYgJycHFgsFlgsFuTm5mL16tVN20tLSzFlyhQkJSUhIiICgwcPxrJlyy7ZZo8ePSBJ0kW3X/3qV5e3R37EwEJKEkLg8RUFuGNYKvpzEGZIaPzQ4l/ZoYjXEbqYTyO5UlJSMG/ePGRlZUEIgUWLFmHChAnYsWMH+vXrh6lTp6K6uhorV65EXFwcFi9ejEmTJmHbtm0YNGhQi21u3boVHo+n6f6ePXswevRo3H777e3bM6Ig84/1h9E32YIx/ZKULoU6QeMHFv/CDlU1DS5YTBxsfT6feljGjx+PcePGISsrC7169cKcOXNgNpvxzTffAAA2bdqEBx54AMOHD0fPnj3x+OOPIzo6Gvn5+a22GR8fj6SkpKbbqlWrkJGRgeuuu659e0YURP711VHY3R5MH5GudCnUSXYUVmNgWrTSZZCCONGjucs+Ce7xeLBkyRLU1dUhNzcXAJCXl4f33nsPlZWVkGUZS5Ysgd1ux/XXX+9Vm06nE++88w7uvvvuNv+hHA4HrFZrs5s/8UrNpJTPD5Th0OlaPPqD3kqXQp1ox8lqDErtonQZRKrhc39TQUEBcnNzYbfbYTabsXz5cmRnN17SfunSpbjjjjsQGxsLnU6H8PBwLF++HJmZmV61vWLFClRXV+Ouu+5q87lz587F008/7Wv57cKF46izHa+owxsbj+H1qUP511aIOXTahl6JZqXLIAU0OD1cELIFPvew9O7dGzt37sSWLVswc+ZMTJs2Dfv27QMAPPHEE6iursa6deuwbds2PPzww5g0aRIKCgq8anvhwoUYO3Ysunbt2uZzZ8+ejZqamqZbYWGhr7tCpGp2lwd/WFGAP92Ww19eIUaWBQQAnZYzwUJRGReNa5HPPSwGg6Gpx2TIkCHYunUr5s+fj0cffRQvvfQS9uzZg379+gEABgwYgK+++govv/wyXnnllUu2e+LECaxbtw4ffPCBV3UYjUYYjfwHpeDkkQWeWLEH91ydjq5cOCzkFNdwwbhQVmbjsvwtaXd8l2UZDocD9fX1jQ1qmjep1Wohy3Kb7bz55ptISEjAD3/4w/aW5BccwkKd5XCZDff9extGZMbhxj6JSpdDCjhSXoee8RFKl0EKaVw0jn+QX8inHpbZs2dj7NixSEtLg81mw+LFi7FhwwasWbMGffr0QWZmJu677z48//zziI2NxYoVK7B27VqsWrWqqY2RI0di4sSJuP/++5sek2UZb775JqZNmwadTr3TuDiEgPypuLoBL6w7CLdH4Imbs9E9lh9YoepkZT168N8/ZJ22OtAnKVLpMlTHp3RQVlaGqVOnoqSkBFFRUcjJycGaNWswevRoAMAnn3yCWbNmYfz48aitrUVmZiYWLVqEcePGNbVx5MgRVFRUNGt33bp1OHnyJO6+++4O2CWiwLNu32n8+5sTmDW2D/omW5QuhxR2usaOIWmcIRSqyqx2XJsVp3QZquNTYFm4cOElt2dlZbW5su3x48cvemzMmDEQnDdMIer9bYXYdOQMXp86lMvtEwDA4fYgTM+fhVBVarVzheMW8H8EkUIKK+vx8NKdOFlZj+dvH8CwQk26RBhw2upQugxSgMsjo7LOiSiTXulSVEe9A0ZUhj1A1BFq6l14/aujKCiqQUKkEdNye2BAarTSZZHKXJkeg68Pn0FuBq/UHGq+PFiO63rFK12GKjGwEHWCYxV1+GD7Kew+VYP7ru2J347pxYXgqFX9u0VjwYYjSpdBCli5qxh/GNdX6TJUiYGFyE9q6l1Yuq0QXx2uQEoXE340sBseHs2gQm0z6DQQovH0gJ6Lx4UMm92FeqeH41dawcBC1MGKqxuwcOMxHKuow+ThabhrRA9+6JDPcjNisWZvKW7OaXvlbwoOq/eUYuwVvBp7axhYiDqAEALbTlThP9+ehMsjcPeIHhjEaanUDj+7qjvufmsrBqZGI6VLuNLlUCf4dG8p5v9kkNJlqBYDiw/YlU8XOl5Rhw92FGH7iSoMTovGQ6N6ITWGHy7UfmF6Lf784xz87v3d+PsdA5EUxdMEwaykpgGWMD0ijPxYbg2PDJGXhBA4XFaLb45VYn+JFYWV9UjpYsKtg1Pw0KgsBlrqcCldwvHcrf3x2/d34p93DuFU18uw+cgZaCQgwqhDuEHb9DXcoINWo57/sx/uLMYtA3n671IYWIhaIIRAcY0d+4qt2Ftcg73FVrg8MtLjIpCXEYcb+ySga1QYQwr5XXpcBH73gz54+L2dWPCzIVyvxwcOtwfPfrwPEwZ2RZ3Dg3qnG7Vnv9Y7Pa0uV6HTaBBu1CLCoPv+63lh59x909ng8/33Wpj02sv6vfD14Qrce3V6e3c5qDGweEkIXksomHlkgY2HK/DlwXIcr6iDLAS6RpuQ3dWCa3vFY+b1GTDqtEqXSSFqYGo0Jg9Pw+wPCvD87TkMyl6y2d24omsUfnFthk+vc7plNDg9qHO6Ue90o85x9vuzX8trHah3etDgdKPO6UGD8/sQZHfJaP1yuVJjsNF/H3DCDVq4PAJZCZHQcXD+JTGw+IC/IoJPTYMLCzcew/YTVcjLjMXk4alIjzOrqquYCABGZSeixGrH858ewO9+0EfpcgKCze5GZJjvH3MGnQYGnQZR4R17Ck6WBRpcnrNhx4N617mQ40G/rlEd+l7BiIGFQpIsCyzceAxfHa7Az69J5xgUCghTruqOeau/w+ItJ/HTK9OULkf1rA0uRIapZ9yPRiMhwqjjwNrLxP4nCjllVjt++e52mAxaLJo+DNdkxTOsUMB49Ae98c3RM9h6vFLpUlTvcntYSJ0YWLwkWj0nSYFk/f7TeHjpLjw0uhd+dlV3BhUKOBqNhDkTr8AL6w7C5ZGVLkfVbHYXA0sQYWDxAT/bAtt/vj2JtftOY+FdQ9E7KVLpcoguW2SYHrcOSsE735xQuhRVs9ndsHAqeNBgYKGQ8OHOIuw8WY3nJvbnbB8KChMHdcPnB8pRWedUuhTVsrKHJagwsFDQW7fvNNbvL8Nzt/aHhrN/KEhoNBJ+PTILf197UOlSVMtqd8OiokG31D4MLF5qZX0hUrlNhyvwfn4hnr99AKcqU9AZ0r0LztQ5UGa1K12KKnEMS3BhYPGBxJVYAsquwmq8/tVRvHDHIK4OSkFr+oh0LPz6mNJlqFLjLCH2sAQL/hanoFTrcGPe6u8wf/IgmAwcs0LBa1iPGBwotaGmwaV0KarDHpbgwsBCQWnuJ/vxwMhMnr+mkDB5eBr+m39K6TJUxyML6LncfdDgv6SXOIYlcGw4UAaDToO8jDilSyHqFNf3jsfGQ+VKl0HkVwwsPuA6LOrn9sh47cujeJTXWqEQYtRpEW7Uoaaep4UoeDGwUFBZu+80RmcnctwKhZz+3aKwp7hG6TJUhT3jwYWBhYLKqaoG9E7kKrYUesZkJ+LjghKly1ANwbQSdBhYKKjsL7UiMSpM6TKIOl3PeDMKK+v5QX2W3SUjjD2tQYWBxUv8FaB+RdUNcHkEMuLNSpdCpIhu0SaU2RxKl6EKNrsLFk5pDioMLD7gmFt121tUgxEZsUqXQaSY1JhwFFbWK12GKli5aFzQYWChoLHlWCWu6BaldBlEislJicKy7UVKl6EKNrsLkUb2sAQTBhYKCrIscPC0jYGFQto1WfEorWlQugxVsNrdsJjYwxJMGFi8xIFs6lZqtSOli0npMogUF27Qod7pVroMxXFZ/uDDwOIDLhynXl8dKufKtkQAMuIjcLS8TukyFMcLHwYfBhYKCl8eqsA1WQwsRHGRRpTW2JUuQ3HsYQk+DCwU8OocbjhcHkSHG5QuhUhRTreMF9YdQhLXIoK1wc3AEmQYWLzEESzq9fHuEozrn6x0GUSK00hAZZ0T6XERSpeiuFqHGwZeqTmo8F/TJxzEojYeWWDFziIGFiIAOq0GP+iXCK2Gv6vuGJaKZz7ej1oHByAHCwYWCmgf7SrGmOxEhOm5BDcRACREhuFMnVPpMhTXN9mCX4/MxK//swN2l0fpcqgDMLBQwLLaXVi6rRCTr0xTuhQi1YgzG1HB5fkBAEO6x2BaXg/8YfkepUuhDsDAQgHrpc8O4/4bM2HUsXeF6ByNxDF357u2VzxcHhllNs6cCnQMLN7ibwBVOVpei8LKeq69QnSBBpcHJp4ibeaHOcn4eHeJ0mVQOzGw+IALx6mDyyPjqZV78cTN2UqXQqQ6suDvqgtd3zseXxwsV7oMaicGFgo489cdwqShqegazaX4iS7UJVyPSg66bcao0yIh0sgrWQc4BhYKKNuOV6LUasf4AV2VLoVIlbQaCR6Z57AvNH5AV3y0u1jpMqgdGFgoYNQ63Pjrpwfx5HieCiJqzY6T1bxqeQtye8Zi85EzSpdB7cDA4gNesFk5TreMR5buwsNjesHCC5oRtarB5UGUif9HLqTTatAzLgKHTtuULoUuEwOLtySAU4WU4fbIeOT9XfjxkBQM6xGjdDlEqhZu0OJMLddhacmkYalYtPm40mXQZeKVobwk4fJ7WIQQ+OZoJb4rtaKi1oGqehdMei3MRh26RochNSYcyVEmJFnCYDJwOuL57C4PHlu2G6OyEzEqO1HpcohU7wf9krB232n8ZDgXVLxQv65ROFPrRHF1AwftByAGFi9JknRZ/Sv7iq3429oD6JtswfD0GFzVMxbR4Xo4XDKsdheKq+3YU1SD9fvLUFzdgAaXB+lxERjZJxFX9oyBPoQv3lVS04BH/7sbP7+mJ67tFa90OUQBoW+yBUu3FSpdhmrNvD4Dr35xBE9PuELpUshHDCxe8rWH5XhFHf6y5gDMRh2e+dEVSI5qOc3npDS/L4TAkfJarN1Xhn9uOIyx/ZNx+5CUkLtWTv6JSvz104OYM7E/rzxL5IM4s4FL819CTko0/vn5EZy22pFoCVO6HPIBA4uXNBpAeNnHcqyiDlMWbsGSX1yFlC7hPr2PJEnITIhEZkIkfn5NOj7aXYx7Fm3Fdb3iceeV3RFhDO5/slqHG8vyT2HLsTN4dcoQRHKALZFPDDoN6py8QvGl/OK6nnj1i6OccRhggvvTrwNJkC7qYRFC4FRVA2oaXLDaXaiqc2F/iRUFRTVYNjOv3eldp9Vg4qAUTBjQDZ/uK8WMd/IxtHsM7srrgajw4Psg/9+eEry9+QR+NKgbXpo8GBoNl+sk8lW4QQe7S1a6DFUbnNYFCzYcQZnNjoRI9rIECgYWL0nnXVDM4fZg6dZCrNl7Gj3iwhEbYURkmA5dwg24sW8CfjumF6QOXBtbo5Fw0xXJ+EG/JHx5qAIPLd2JnnER+Pm1PYOiS3N/iRXz1x1CWmw43po+HAZd6I7bIeoIJr0WDreHFwa9hPuu7YnXvzyKP/yQvSyBgoHFB0IIFFU3YNay3fjRwG54a/ow6DpxUKwkSbiuVzyu6xWP/BNVePLDPegRG4EHR2YF7Kmi/+0pxQfbT+H/bunHUftEHcQtC0hgD+WlDO0Rg1e+YC9LIAnMTzkFSJKEoqoGPLJ0F/50Ww7SYn0bm9LRhnTvglenDMWXB8vx87e3YURmHG4bnIKkKP/8x6tzuLHjZDXqnG4YtBoYdBroz37VaSTUNLhQZrOj3ulBhEGHCKMOkWE6xJmNSOliaho07JEFahpc2FVYjSVbTyItJhwv3zk4pGdDEXW0pCgjymx2n8fQhZpf3ZCJF9cfxjM/4oyhQMDA4qVu0Sa8+fUx/OMng5Aao55fAtf2iseIzDh8fbgCc1fvh93lwc05XXFlzxif/mqw2V04bbWjtMaBkpoGlNkcOFPrRE2DC2fqHAg3aDE4rQssYXo4PDJcbhnOs19dHhlR4QYkRBqRZNGj3unBmVoHjlXUoqLWiVNV9XC4ZEhSY/CzhOnRK9GMZ3/UH/GRRj8eHaLQ1DfZgv0lNgaWNgxK64I3vz6OYxV1nI0YACQhgmPBeavViqioKNTU1MBisShdjmKq651YvacU209UoaLWAY0kwWTQItEShlizAW6PQJ3DjfJaB6rrXQAap2xHGHVIigpDkiUMSVFhjc+PMCDKpIfFpIeWA2CJAsb2k1XYeKgCD47MUroU1TtaXosX1h3CPyYPUrqUkOXt5zd7WIJMdLgBk4enYfJ5q1zWOdwoszlQUeuAXqtBhEGLOLMR0eH6Dh0cTETq0DsxEgu/OqZ0GQGhZ7wZUSY9dhZWY2BqtNLl0CVw4EAIiDDqkB4XgWE9YjAwNRpZiZHoEmFgWCEKUhFGHddi8cEDIzPx4vpDCJITDkGLgYWIKAglRBpRXN2gdBkBISEyDP26RWHDwXKlS6FLYGAhIgpC1/aKx1eH+AHsrV9c2xMLvzoGj8xeFrViYCEiCkJXZ8Zh4+EzSpcRMMxGHUZnJ2LFjiKlS6FWMLAQEQWh6HADbHYX3B4u0++tycPTsGz7KdhdHqVLoRYwsBARBalhPWKw5Vil0mUEDINOg8nD0/DvzSeULoVawMBCRBSkbs5JxqrdxUqXEVB+2D8ZXx4qR83ZdapIPXwKLAsWLEBOTg4sFgssFgtyc3OxevXqpu2lpaWYMmUKkpKSEBERgcGDB2PZsmVttltUVISf/exniI2NhclkQv/+/bFt2zbf94aIiJp0j41Auc2Byjqn0qUEDI1GwszrMrDgiyNKl0IX8CmwpKSkYN68ecjPz8e2bdtw4403YsKECdi7dy8AYOrUqThw4ABWrlyJgoIC3HrrrZg0aRJ27NjRaptVVVUYMWIE9Ho9Vq9ejX379uGvf/0runTp0r49IyIi3JWXjrc2HVe6jICSlxmHYxW1nBauMu1emj8mJgZ/+ctfcM8998BsNmPBggWYMmVK0/bY2Fj86U9/wr333tvi62fNmoWvv/4aX331VXvK4NL8REQtEEJg2ptb8c87B8McoFd1V8K+Yive3nwc827LUbqUoOft5/dlj2HxeDxYsmQJ6urqkJubCwDIy8vDe++9h8rKSsiyjCVLlsBut+P6669vtZ2VK1di6NChuP3225GQkIBBgwbh9ddfb/P9HQ4HrFZrsxsRETUnSRJ+OjwNi7dwIKkvsrta4JYFDp62KV0KneVzYCkoKIDZbIbRaMSMGTOwfPlyZGdnAwCWLl0Kl8uF2NhYGI1G3HfffVi+fDkyMzNbbe/o0aNYsGABsrKysGbNGsycORMPPvggFi1adMk65s6di6ioqKZbamqqr7tCRBQSxmQn4ouD5Zzi7KNfj8zCC+sOKl0GneXzKSGn04mTJ0+ipqYG//3vf/Gvf/0LX3zxBbKzs/HAAw/g22+/xXPPPYe4uDisWLECf//73/HVV1+hf//+LbZnMBgwdOhQbNq0qemxBx98EFu3bsXmzZtbrcPhcMDhcDTdt1qtSE1N5SkhIqIWvPTZIQzpHoPcjFilSwkoz67ahzH9kjA8PUbpUoKW304JGQwGZGZmYsiQIZg7dy4GDBiA+fPn48iRI3jppZfwxhtvYOTIkRgwYACeeuopDB06FC+//HKr7SUnJzf10JzTt29fnDx58pJ1GI3GptlK525ERNSyMf2S8Om+UqXLCCglNQ3QaTV48bNDSpdCANo9AkuWZTgcDtTX1wMANJrmGUir1UKWW++GHDFiBA4cONDssYMHD6J79+7tLY2IiM7KSjDj4GkbGpwemAxapctRLavdhf8VlOLTfaWwmPSYMLAbHhnTS+myCD4GltmzZ2Ps2LFIS0uDzWbD4sWLsWHDBqxZswZ9+vRBZmYm7rvvPjz//POIjY3FihUrsHbtWqxataqpjZEjR2LixIm4//77AQAPPfQQ8vLy8Nxzz2HSpEn49ttv8dprr+G1117r2D0lIgphkiTh3mt64vlPD+CJm7PbfkEIcbplbDhQho92l8Dp9mDsFcmY/5NBiOCsKlXx6V+jrKwMU6dORUlJCaKiopCTk4M1a9Zg9OjRAIBPPvkEs2bNwvjx41FbW4vMzEwsWrQI48aNa2rjyJEjqKioaLo/bNgwLF++HLNnz8Yf//hHpKen44UXXsCdd97ZQbtIREQAcEPvBHy6txTfHqsM+TEZQgjkn6jC8h1FOFXVgOt7x+PJm7MRH2lUujRqRbvXYVELrsNCRNQ2q92F+97Ox8K7hiLcEHo9CIfLarFiRxF2FFZhSFoXTBjUDRnxZqXLCmnefn6H3k8rEVEIs4Tp8eDILDy2rADz7xgIjUZSuiS/K7PZ8dGuEnxxsBzdY8Lxo0Hd8NsxvSBJwb/vwYSBhYgoxORmxOJwmQ3z1x/CQ6ODc0BpvdONNXtL8UlBKcINWozP6Yp/TR0Kg47X/A1UPCVERBSChBB46bPDqGlw4ffj+gZFT4tHFth85AyW7yhCTYMTY7KTcFP/JFjC9EqXRpfg7ec3AwsRUQh7b+tJbDlaibm39YdRF5jTnb8rtWL59iLsLbYiNyMWEwZ2RUqXcKXLIi9xDAsREbXpjmFpiDMbMfOd7Zh3W38kRIYpXZJXymx2rNxZjC8OliMj3oxbB3fDrLF9OC4liDGwEBGFuJF9E5HSJRy/WbITP7+mJ27ok6B0SS1qcHrw6b5SfLy7BGF6LSYM7IppeT2g13JcSijgKSEiIgLQGAj+vu4gKmod+MO4vog1K78miSwLfHO0cVxKZZ0To7MTMbZ/MqJMHJcSLHhKiIiIfGIyaPH7cX2x+1Q1fr1kJ24d3A0TB3VT5DTLwdM2fLC9CAVF1bgqPRYPjsxCagzHpYQy9rAQEdFFXB4Z//rqGPJPVOHRm3qjV2Kk39+z3ObAyl3F2HCgDOlxEZg4qBsGpkZzXEqQYw8LERFdNr1Wg5nXZ+BUVT2u/tPnWHzvlcjLjOvw97G7PPh032l8srsEep0G43OSsXDaMK6XQhdhYCEiolaldAnHB7/Mw18/PYD0+AgkR5na3aYsC2w5VokVO4pQZrNjdHYS/nRbDqLCOS6FWsfAQkRElzQ4rQuemXAFnl65D7FmA34/ru9lXcn4cFktlu84hZ2F1RjeIxa/uiETabEcl0Le4RgWIiLy2jdHz+DFzw7h2R/1R3pcRJvPP1PrwEe7ivHZgXKkxZgwcVAKBqdxXAp9j2NYiIiow13VMxapMeH4v5V7MTitC+69Jv2idVDsLg/W7T+Nj3eXQKORMD6nK16fOiRgV9IldWAPCxER+UwIgRU7i7B06yncPCAZtw1Owa7CaqzYWYSSGjtG9U3EzTnJiA43KF0qqRyvJURERH7n8shYubMYK3cVY0BqNCYO6ubVqSKic3hKiIiI/E6v1eC2ISm4bUiK0qVQkONEdyIiIlI9BhYiIiJSPQYWIiIiUj0GFiIiIlI9BhYiIiJSPQYWIiIiUj0GFiIiIlI9BhYiIiJSPQYWIiIiUj0GFiIiIlI9BhYiIiJSPQYWIiIiUj0GFiIiIlI9BhYiIiJSPZ3SBXQUIQQAwGq1KlwJEREReevc5/a5z/HWBE1gsdlsAIDU1FSFKyEiIiJf2Ww2REVFtbpdEm1FmgAhyzIOHDiA7OxsFBYWwmKxKF2SKlmtVqSmpvIYtYLHp208RpfG49M2HqO2hdIxEkLAZrOha9eu0GhaH6kSND0sGo0G3bp1AwBYLJag/wduLx6jS+PxaRuP0aXx+LSNx6htoXKMLtWzcg4H3RIREZHqMbAQERGR6gVVYDEajXjqqadgNBqVLkW1eIwujcenbTxGl8bj0zYeo7bxGF0saAbdEhERUfAKqh4WIiIiCk4MLERERKR6DCxERESkegwsREREpHoBFVgOHjyICRMmIC4uDhaLBVdffTU+//zzpu1nzpzBTTfdhK5du8JoNCI1NRX3339/m9cXuuWWW5CWloawsDAkJydjypQpKC4u9vfu+IU/jtHx48dxzz33ID09HSaTCRkZGXjqqafgdDo7Y5c6lL9+hubMmYO8vDyEh4cjOjraz3vhX/46RpWVlbjzzjthsVgQHR2Ne+65B7W1tf7eHb9o6xjt2rULkydPRmpqKkwmE/r27Yv58+e32e727dsxevRoREdHIzY2Fr/4xS8C8hj56/i01W4g8ccx2rBhAyRJavG2detWf++S3wVUYLn55pvhdrvx2WefIT8/HwMGDMDNN9+M0tJSAI2r3U6YMAErV67EwYMH8dZbb2HdunWYMWPGJdu94YYbsHTpUhw4cADLli3DkSNH8OMf/7gzdqnD+eMYfffdd5BlGa+++ir27t2Lv//973jllVfw+9//vrN2q8P462fI6XTi9ttvx8yZMztjN/zKX8fozjvvxN69e7F27VqsWrUKX375JX7xi190xi51uLaOUX5+PhISEvDOO+9g7969+MMf/oDZs2fjpZdearXN4uJijBo1CpmZmdiyZQv+97//Ye/evbjrrrs6aa86jj+OjzftBhJ/HKO8vDyUlJQ0u917771IT0/H0KFDO2vX/EcEiPLycgFAfPnll02PWa1WAUCsXbu21dfNnz9fpKSk+PReH374oZAkSTidzsuuVwmdeYz+/Oc/i/T09MuuVQmdcXzefPNNERUV1d5SFeOvY7Rv3z4BQGzdurXpsdWrVwtJkkRRUVHHFN9JLvcY/fKXvxQ33HBDq9tfffVVkZCQIDweT9Nju3fvFgDEoUOHOqb4TuCv43O57aqRv47RhZxOp4iPjxd//OMf21WvWgRMD0tsbCx69+6Nt99+G3V1dXC73Xj11VeRkJCAIUOGtPia4uJifPDBB7juuuu8fp/Kykq8++67yMvLg16v76jyO0VnHSMAqKmpQUxMTEeU3Wk68/gEKn8do82bNyM6OrrZX3mjRo2CRqPBli1bOnw//OlyjhHQ9v8Zh8MBg8HQ7OJvJpMJALBx48aO2wE/89fxudx21chfx+hCK1euxJkzZzB9+vSOKFt5SicmXxQWFoohQ4YISZKEVqsVycnJYvv27Rc97yc/+YkwmUwCgBg/frxoaGhos+1HH31UhIeHCwDiqquuEhUVFf7YBb/z5zE659ChQ8JisYjXXnutI0vvFP4+PoHewyKEf47RnDlzRK9evS56PD4+Xvzzn//s0Po7g7fH6Jyvv/5a6HQ6sWbNmlafs2fPHqHT6cSf//xn4XA4RGVlpbjtttsEAPHcc8/5Yzf8xh/H53LaVTN/HaPzjR07VowdO7YjylUFxQPLY489JgBc8rZ//34hy7K45ZZbxNixY8XGjRtFfn6+mDlzpujWrZsoLi5u1mZJSYnYv3+/+PDDD0V2draYOXNmm3WUl5eLAwcOiE8//VSMGDFCjBs3Tsiy7K/d9olajpEQQpw6dUpkZGSIe+65xx+7elnUdHzUGliUPkaBEFj8cYyEEKKgoEDExcWJZ555ps0a3n33XZGYmCi0Wq0wGAzikUceEYmJiWLevHn+2GWfKH18fG1XCUofo/MVFhYKjUYj/vvf/3bkLipK8cBSVlYm9u/ff8mbw+EQ69atExqNRtTU1DR7fWZmppg7d26r7X/11VcCgE8/0IWFhQKA2LRp02XvV0dSyzEqKioSWVlZYsqUKc3OsytNLcdHCPUGFqWP0cKFC0V0dHSzx1wul9BqteKDDz5o/w52AH8co71794qEhATx+9//3qdaSktLhc1mE7W1tUKj0YilS5e2e//aS+njc7k/m51J6WN0vj/+8Y8iPj4+4MZiXoqunWeU2i0+Ph7x8fFtPq++vh4Amp3fPXdfluVWX3dum8Ph8Lqmy3mNP6nhGBUVFeGGG27AkCFD8Oabb170HkpSw/FRO6WPUW5uLqqrq5Gfn990jv6zzz6DLMu48sorvdoHf+voY7R3717ceOONmDZtGubMmeNTLYmJiQCAN954A2FhYRg9erRPr/cHpY/P5f5sdialj9E5Qgi8+eabmDp1asCNxbwkpROTt8rLy0VsbKy49dZbxc6dO8WBAwfEI488IvR6vdi5c6cQQoiPP/5YvPHGG6KgoEAcO3ZMrFq1SvTt21eMGDGiqZ0tW7aI3r17i1OnTgkhhPjmm2/Eiy++KHbs2CGOHz8u1q9fL/Ly8kRGRoaw2+2K7Ovl8tcxOnXqlMjMzBQjR44Up06dEiUlJU23QOKv4yOEECdOnBA7duwQTz/9tDCbzWLHjh1ix44dwmazdfp+toc/j9FNN90kBg0aJLZs2SI2btwosrKyxOTJkzt9H9vLm2NUUFAg4uPjxc9+9rNm/1/Kysqa2mnpGL344osiPz9fHDhwQLz00kvCZDKJ+fPnd/o+toe/jo837QYKf/4MCdHYG4Wzp5+CScAEFiGE2Lp1qxgzZoyIiYkRkZGR4qqrrhKffPJJ0/bPPvtM5ObmiqioKBEWFiaysrLEY489Jqqqqpqe8/nnnwsA4tixY0KIxmmDN9xwg4iJiRFGo1H06NFDzJgx46IfgEDhj2P05ptvtno+NtD44/gIIcS0adNaPD6ff/555+1cB/HXMTpz5oyYPHmyMJvNwmKxiOnTpwdcoDunrWP01FNPtfjz0L1796bntHSMpkyZImJiYoTBYBA5OTni7bff7sS96jj+Oj5ttRtI/HWMhBBi8uTJIi8vr5P2pPNIQgjh714cIiIiovZQz0AEIiIiolYwsBAREZHqMbAQERGR6jGwEBERkeoxsBAREZHqMbAQERGR6jGwEBERkeoxsBAREZHqMbAQERGR6jGwEBERkeoxsBAREZHqMbAQERGR6v0/Gzgj1ZNA1PIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the surrounding operation 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "special for OH , I believe that [1006, 925, 926, 927, 928, 935, 937, 986, 988] are surrounded by vtd (fragment) 933 . Here, let me show you\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the surrounding operation 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "special for OH , I believe that [3739] are surrounded by vtd (fragment) 3622 . Here, let me show you\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the surrounding operation 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After surround checks, we appear to have 10008 building blocks defined. We captured 833 surrounded VTDs\n",
      "working on unit neighbor lists for county 0\n",
      "working on unit neighbor lists for county 20\n",
      "working on unit neighbor lists for county 40\n",
      "working on unit neighbor lists for county 60\n",
      "working on unit neighbor lists for county 80\n",
      "All done creating unit lists\n"
     ]
    }
   ],
   "source": [
    "#3/2/24 - split up all multipoly VTDs into fragments, divvy pop by area\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, countyUnits, borderUnits = list(),list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    countyUnits.append(c)\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDset = [set(countyTractList[c]) for c in range(nCounties)]\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "            geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "            for i, geo in enumerate(geos):\n",
    "                if i == 0:\n",
    "                    fragVTDgeom[v] = geo\n",
    "                    fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                else:\n",
    "                    fNo = len(fragVTDgeom)\n",
    "                    fragVTDgeom.append(geo)\n",
    "                    fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                    parentVTDno.append(v)\n",
    "                    countyFragVTDset[c].add(fNo )\n",
    "                    VTDchildren[v].append(fNo)\n",
    "                    nVTDneighbors.append(0)\n",
    "                    lastVTDneighbor.append(-999)\n",
    "                    \n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\")\n",
    "\n",
    "debugV = -7616\n",
    "for kk,V in enumerate(populatedTractList):\n",
    "    if kk%500 == 0:\n",
    "        print(\"looking for surrounds, now working on vtd\",V)\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties:\n",
    "        for v in VTDchildren[V]:\n",
    "            for vv in countyFragVTDset[c].difference({v}) :\n",
    "                if isRookAdj( fragVTDgeom[vv],fragVTDgeom[v] ):\n",
    "                    nVTDneighbors[v] +=1\n",
    "                    lastVTDneighbor[v] = vv\n",
    "                    if v == debugV:\n",
    "                        print(\"I just added neighbor\",vv,\"to magicV\",v)\n",
    "                #if nVTDneighbors[v] >= 4:  #Enough to have >=2 even after surrounds.  Will do full neighbor list later\n",
    "                #    break\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if isRookAdj( fragVTDgeom[v],countyGeom[cc] ) :\n",
    "                        if cc not in unitCounties + allFusedCounties:\n",
    "                            for vv in countyFragVTDset[cc] :\n",
    "                                if isRookAdj(fragVTDgeom[vv], fragVTDgeom[v]):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fragVTDgeom[v],2)\n",
    "                                plotCenter(\"iso\",fragVTDgeom[v])\n",
    "                                plotPoly(countyGeom[cc])\n",
    "                                plt.show()\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                raise Exception(\"neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "            if v == debugV:\n",
    "                print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for V in populatedTractList:\n",
    "        c = countyNo[V]\n",
    "        if c not in allFusedCounties and c not in unitCounties: \n",
    "            for v in VTDchildren[V]:\n",
    "                if nVTDneighbors[v] == 1:\n",
    "                    vv = lastVTDneighbor[v]\n",
    "                    if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                        for sNo, s in enumerate(surroundedVTDs):\n",
    "                            if surrounders[sNo] == v:\n",
    "                                surrounders[sNo] = vv\n",
    "                    surroundedVTDs.append(v)\n",
    "                    surrounders.append(vv)\n",
    "                    nVTDneighbors[vv] -=1\n",
    "                    nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                    fragVTDpop[vv] += fragVTDpop[v]\n",
    "                    fragVTDpop[v] = 0\n",
    "                    if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                        for vvv in countyFragVTDset[c]:\n",
    "                            if vvv not in [v,vv]:\n",
    "                                if isRookAdj( fragVTDgeom[vvv], fragVTDgeom[vv]):\n",
    "                                    lastVTDneighbor[vv] = vvv\n",
    "                    if lastVTDneighbor[vv] == v:\n",
    "                        print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                    if loop > 0:\n",
    "                        print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "if STATE == \"OH\":  #state-specific manual surround enforcement\n",
    "    #c = 72\n",
    "    #print(\"visualizing VTD fragments in county\",c)\n",
    "    #for v in countyFragVTDset[c]:\n",
    "    #    plotPoly(fragVTDgeom[v])\n",
    "    #    plotCenter(v, fragVTDgeom[v])\n",
    "    #plt.show()\n",
    "    #print(\"I recommend surrounded = [334, 343] and surrounder = 312\")\n",
    "    #specialSurrounded = ast.literal_eval(input(\"enter the [v1, v2, ...] list of surrounded fragVTDs from above map\"))\n",
    "    #specialSurrounder = int(input(\"enter the fragVTDno that surrounds these\"))\n",
    "    specialSurroundedLists = [ [334, 343] ] + sdedLists\n",
    "    specialSurrounders = [312] + sders \n",
    "    for ijk, specialSurrounded in enumerate(specialSurroundedLists):\n",
    "        specialSurrounder = VTDchildren[specialSurrounders[ijk]][0]\n",
    "        print(\"special for\",STATE,\", I believe that\", specialSurrounded,\"are surrounded by vtd (fragment)\",specialSurrounder,\". Here, let me show you\")\n",
    "        c = countyNo[parentVTDno[specialSurrounder]]\n",
    "        plotPoly(countyGeom[c], 0.5)\n",
    "        for V in specialSurrounded:            \n",
    "            for v in VTDchildren[V]:\n",
    "                plotPoly(fragVTDgeom[v])\n",
    "                plotCenter(v,fragVTDgeom[v])\n",
    "            plotPoly(fragVTDgeom[specialSurrounder],0.2)\n",
    "            plotCenter(specialSurrounder, fragVTDgeom[specialSurrounder], 8)\n",
    "        plt.show()\n",
    "        shouldIcombine = int(input(\"enter 1 to apply the surrounding operation\"))\n",
    "        if shouldIcombine == 1:\n",
    "            for v in specialSurrounded:\n",
    "                vv = specialSurrounder\n",
    "                if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                    for sNo, s in enumerate(surroundedVTDs):\n",
    "                        if surrounders[sNo] == v:\n",
    "                            surrounders[sNo] = vv\n",
    "                surroundedVTDs.append(v)\n",
    "                surrounders.append(vv)\n",
    "                nVTDneighbors[vv] -=1\n",
    "                nVTDneighbors[v] = 0 #we are capturing all surroundees (some of whom may touch other surroundees)\n",
    "                fragVTDpop[vv] += fragVTDpop[v]\n",
    "                fragVTDpop[v] = 0\n",
    "\n",
    "\n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] >1:\n",
    "                unitCP.append(fragVTDgeom[v].centroid)\n",
    "                unitGeom.append(fragVTDgeom[v])\n",
    "                unitPop.append(fragVTDpop[v])   #for now, ratio pop by area, not block decomposition\n",
    "                vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "                allUnits.append(v)\n",
    "#for V in populatedTractList:\n",
    "#    c = countyNo[V]\n",
    "#    if c not in allFusedCounties and c not in unitCounties:  \n",
    "#        for v in VTDchildren[V]: \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "                print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "            if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "                print(\"WARNING. unsurrounded vtd\",v,\"had no found neighbors\")\n",
    "\n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    for v in countyFragVTDset[c]:\n",
    "        if v in allUnits:\n",
    "            countyUnitList[c].append(allUnits.index(v))\n",
    "\n",
    "sketchyList = list()\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if isRookAdj( countyGeom[c], unitGeom[uu] ):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if isRookAdj( geo, countyGeom[c] ):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if isRookAdj( unitGeom[u], unitGeom[uu] ):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if isRookAdj( unitGeom[u], unitGeom[uu] ):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if isRookAdj( geo, unitGeom[u] ):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, this unit appears surrounded.  Next block to confirm it has non-intracounty neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if isRookAdj( geo, CCBgeom[ii] ):\n",
    "            unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "            unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "94741893-6835-45ac-9793-64cbaf4823b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "for L in sketchyList:  #hope that some of these flagged units picked up unit-county or CCB neighbors\n",
    "    print(\"sketchy unit\",L[0], \"has neighbor list\",unitNbrs[L[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "08e639ac-3b6c-4b3f-9cb7-c510b445445b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "which VTDs adjoin Sandusky Bay\n"
     ]
    },
    {
     "data": {
      "image/png": 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LC2AKdPr27cuoUaOKVGeuS5cusWfPHjZt2nRP2/78809iYmIIDAzEysqKbdu2MX36dN5///3iPKJQAuaMzBZg0OuxsrPHpXJVLhz8l4YdOlusbsGybNu0IeHHReguXMSqbh3zcYVWi2OfPiSvW4f76LdRaDRl2EpBEITbLJ6Red68eTRp0oRhw4YB0L59e5o0acKGDRvMZcLCwoiPL/4+TYsXL6ZKlSp06dLlnnNqtZrvvvuOoKAgAgICmD9/PrNmzWLChAklfxihSHKHtyxBrdWSlZZKjYBmhJ84Loa4yjHrpk2RtNp8h7icnh2A4eZNsQmpIAjliiTfb033YyYlJQVHR0eSk5PF/J5ikGWZWc/3BOC9VRtLXI/RYOCnD0ah1mrp/f54FrwxhG5vvCN6e8qxq4OHoHRwoMrcb+45d+WZAagrVaLKN3PKoGWCIDxOivr5LfbeEkpNkiTz16WJoU/u2EJi5A2eHDYKe1c3KtdrwPkDey3RROEB0fj4oLt8Od9zjr16krpzp8jZIwhCuSGCHsGiSrr/li4jg/2/LadBuyfwrFkbgDqB7bh6MoSsNDHEVV5ZN21KdlgYhuTke8459u1rytmzSux/JwhC+SCCHsGisrMK34pClmXCjv7H5WOH+frF3vw25SMOb1iLPjOTts/fTkVQp1VrjEYDl46IvZzKK+vGpsUGqTt23nNOaW+PY9++3Fy5CmN29sNumiAIwj1E0CNYVFH234o4f4b1n09m3WeTMBoMXDt1kkPrVtHs6T7Yu7qZy9m5uFK5bgMuiCGuckvt42P6ooBhTecXX8CQmCgmNAuCUC6IoEewCNWtXEj6IvT0HN9kWsnX8eVh5mOdXnmdNs8Nuqds3aC2XA0NITMt1UItFSxJUipRODqSfSP/bObaWrWwbtaMm6tXP+SWCYIg3EsEPYJF2DmbcjAVpadHr8uiUp36NOvRm9fm/shbP6+hSden80yIzuXbqg1Go5Gww2KIqzySlErs2rcn7Z9dBZZxfnYAGQcOkn3t2sNrmCAIQj5E0CNYhK3TraCnCD09SrUGza2d2R09vFBrC95zzc7ZhSr1G3L+4L+WaahgcfZPdER37hz6yMj8z3ftisLOjoTFix9uwwRBEO4igh7BImydnIGiDW8pVCqMxdinrW5gO66FhuTZokIoP2zbtQOVitR//sn3vMLKCqdnnyVl418Ys7IecusEQRBuE0GPYBGOnl4AZKWnF1pWoVAgFyPo8W3VGtkoc0kMcZVLSnt7bFo0L3SIy5iWRvL69Q+tXYIgCHcr1t5bglAQJ09vAFLiYgotq1SpMBQj6LF1cjYNcR3Yi72LKyHbNmHn7Iq9qxtJMVFcPLSfyvUa0HeM2HKkrNi1aUPcd98jy3K+c7M01avj0L07CYsW4zRgAFIJNicWBEEoLdHTI1iEo4cnABf/O1BoWUmhLFZPD0CdoHZcPXmctTMmkJoQz9XQ4/z3xxqunz6JLBu5fPwI6Uk3S9R2oWS6dOmCn58fAQEB9Jw9m9M3b2JISmLz5s00b94cPz8/AgMDOXHiBAAuQwZz7MIFgvz98ff3JyAggJ07783vc/bsWWxsbBg9evRDfiJBEB51oqdHsAhn78oAJEZcL/Cv/VwKpQKjsXhBT8OOndmx6HsABoyfhpWdnflcVnoaC0cO5eimP2j/4pDiN14okdWrV+Pk5ATAytlz+HjsGILOnmXgwIHs2bOHhg0bsnfvXgYOHMipU6ewatyYt+Ni+bJeXQZu2cKFCxcIDg7m/PnzWFubNq3V6/UMHz6cvn37luGTCYLwqBI9PYJF3JlUMCk6/1U8uRRKFcacnGLVr9ZoGfr1fJ4YPCxPwANgZWtHww7BnPt3d7HqFEonN+AByNCoATi/dy+urq40bNgQgHbt2nHt2jWOHTtGQkICiXo9Ta9eQ3flCnXq1MHJyYm///7bXM/kyZMZMGAAvr6+D/VZBEF4PIigR7AISZKo3SIQgPCTx+9bVqFUYjQai30Pl0qVadq9d77nqjZoTGpCHKmJ8cWu93GUlp2GoZi9bfl5+eWXqVq1KhNmzGBWs+ZUycwiISGB/fv3A7BhwwZSU1MJDw/Hzc0N70qV2KLTkbj0Jw4fPsz58+cJDw8H4NChQxw4cIA333yz1O0SBEHIjwh6BIvxf7I7AHtX/ASALiOd07t3ELpzK1dCjpIUE43RaDBNZC7hxqQF8fatC0DUhXMWrfdR9NPpnwj6NYglp5eUuq6ff/6Z69evM3XqVD4PC8POYGDNmjWMGzeOZs2asXXrVho0aIBKZRpJ/2PDBjZo1HT+8gtmz5pF27ZtUalUZGRk8MYbb7Bw4cL7Do0KgiCUhpjTI1iMTyN/wJSrJzEygq3z5xBx7kyeMkqVCpVGi1Kttui97VxccatajbP/7qJOYFuL1v0o2XxlM18e+RKATVc28Vrj1yxS7+DBg/nfq68SsX8/bf43nN27TUONOp0OLy8vGjRoAIC/vz+bt20jrNtTePXuQ+tJE2nYsCFhYWFcu3aNJ554AoCkpCSMRiM3b97kp59+skgbBUEQRNAjWIxCqcQvuBsnt29m5acfkJmaQv+PJuPT2J/U+HhuRkVwMzqSm1ER5gzOltTkqZ5sW/gde3/9iYCuPbB3cSv8osfIzms7+WDPB9R3qc8bAW/w5s43ORB5gKBKQcWuKykpiYyMDCpVqgTA+vXrcXV1xSEnh6MvDyZop2mD0SlTptCpUydq164NQFRUFN4+Ptg/+STfT5mMra0tnTp1QpIk4uLizPVPnDiRpKQkZs+eXfoHFwRBuEUEPYJFtejZn5PbN5uzJ/s08kehUOLo4YmjhyfV/Zs+sHs36vgkcVevELLlL47+tZ5GHZ+kcacueNas/cDuWd5l5WRhlI3YqG3YcW0HnjaefNv5W9yt3Wni0YQvj3zJ2l5ri11vcnIyAwYMIDMzE4VCgbu7O39t2YLHlXA+HjqE0Lp1yTEYCAoKYtGiRebrFixYwPLlyzFmZeGTeJNfFi4Qw1mCIDw0kizLclk3orxISUnB0dGR5ORkHBwcyro5FdauXxZxdOM6bBydGLFg2UO/f2ZaKus/m0xs+GUkhYIRC3657/5ej6q07DT6/NEHvVHPn33/5PP/PudIzBE2998MmOb2zDk2h6MvHbVY4JG2dy/Xhw2n9o7tqCtXvm/Z8IEvAVB9+cP/GREE4dFS1M9vMZFZsLjWA17E1tkF92o1yuT+1nb2vDDlCwZOn4U+K5Ooi+fLpB0lZTQYirRxa2E2XdlETEYMiVmJtPm1DX+E/UH3Gt3N512sXNAb9YTEhZT6XrkMiYkAKF1dCy3r+spQMo8eJTPEcvcXBEG4HxH0CBansbLGtYoPuozC9+F6kFwqVcHG0Ymz/+4q03YUR3JsDD9/+CYL3xhKdmZGqeo6EHmAph5N+bjVx+ZjbzV9y/x19xrd8XP3Y9L+SViqw9eYmQlKJZJWW2hZuyeeQFOtGglLllrk3oIgCIURQY9gcRnJSVwLDaFGQHNO797BoreHsf6LKSTcuP5Q22GaWP0Ul/47UOwM0GXh8vHDLHprGAk3rpGVnkZ2ZuG9PXduBdGuXTuOHzflSNq8eTOrRqxi/Yj1zB4ym2k1p7Gsu2kY6fDhw7Rp04amTZpy+MPDnDhwwtzbs3jxYho3boxKpSrRJGJjegYKa+siDZdJCgUuQ4eSum0buitXin0vQRCE4hJBj2BxCqVpfvyBNSvY/P3XuFapRvSlCyx9bwSRF84+1LZU92tCVnoaMZcvPdT7lsT5fXtwrlSZ4NdGAqC1tS30mtWrV3Py5ElCQkJ49913GTJkCDdv3mTgwIF8OOtDqk2qxtSZU/loxEf4u/sjyzJ9+/Zl0qRJnDhxgh+W/kDEjxEkJCcA0KxZM1avXs2LL75YomeQc3IwpqWZenyKwLFvH1SensTNnlOi+wmCIBSHCHoEi7tzm4jA/i9QN7ANilvJ6dRW1g+1LV6166CxtuHqyZCHet+SiA2/TJX6DdHrslBptag0hQ8R3bkVRHJyMpIkse/EPhS2CjZnbkZGpm7Tunm2goiLiyM4OBiAAzkHUNuqiQsxLRf39/enfv36KBQl+9Wg8vAAQDYULeO2QqvFddhrpG7Zgj7y/tuXCIIglJYIeoQH6timP9j07Ve4V6vBSzNm4+5T/aHeX6lS4dPIj6uFbI1R1nL0ehIjb+BRvSZJMdE4eXgVeUXVyy+/jJu3G6M+GMXIz0by4dkPuZl4k4vHL2Ktsmb3lt15t4Lw9mb16tXEZsSybMsysqOziboeZZHnkHU6UChQ2NoU+RrHnj1RODiQuGy5RdogCIJQEBH0CA+MrbMLLXs/w8DpX9P3w0/LLF9Odf+mRJw/Q/z1q2Vy/6JIio7EaDDgWsWH5JgonLy8i3ztzO9n4j3TG6c+Trz74bsobZS0GdeG6DXRhH4cyoI1C/JuBfHHHyxevJigFkFEb4mmRVAL87nSMqSmoLC3L9YSeKW9PU7PPMPNX3/FmF62k98FQXi0iaBHsLjcScNtnnuJVn2fxatW2e6Y3bBDMNb2Dqz7zHKrlCztZmQEYFpxlhQThaNn0YKejDPxzFvwGZIs8fqrr5N5LpOZzWaSWCWRmuNqUntSbRZ/u5jIyMi8W0Fs3syxY8do9k4zzlw5Q7369SzyHMbUNJT29sW+zvn55yAnh5srV1mkHYIgCPkRQY9gcVmpqQBY2dgVUvLhUGk0WNnakRIXC+U06EmMvIHW1hYrO3tS4mJxKiToiTh3hlVjJ3Dmu70Mj30GvwxfGkU3wsnZiTFHxqBP0uOkdWJr/60snbP0nq0gABy1jgReDSRbmU24R7hFnsOQmoLCofhBj8bHB7vOnUla9zuysWjzgQRBEIpLBD2CxUWHXQQos+SE+bGys6duUDukEk7QfdBuRkXi4l2FlPg4jAYDTp5eBZaNunietTMmoL+u57V1H9F58cvsn3mED2d8iNMbTgR6BxL7eyxXPr5Cu4B2XL169Z6tIOrUqYOvry+he0Jp83Eb5obMJSU7haVLl1KlShV+++03Jk6cSJUqVczL4IvCmJKK0q74QQ+Ay8uDyL4URvK69SW6XhAEoTBi7y3B4m6cO42tkzOO9/ngfthUGnW5HdoCSIy6gbN3JZJiTL0wTl6V8i0XG36ZFePfw7OmL/6+bdhY9Smi7BK4QTQTqn7Pe83f4+UGL3Mp6RL9fPvlSUaYa8KECUyYMMH8/dSDU1l1fhU2KhuGDBnCkCFDSvwchrTUEvX0ANg0bYp9167E//ADjn16IymVJW6HIAhCfsrnn71ChZWVlkbo9s3Uat6qXG0k6exduVxPZL4ZGYFzpSokx0ShUCpxcHO/p0xOdja/jDEFMbWbt6JS20YA2Bi1tEhryNwn5jK44WAkScLf3Z8TcScwGA2FBnvett60NKpR/T0GjEb0sRkkrrlA5ql45JziDTUZU1JR2pd83zqXIYPR37hB2u49Ja5DEAShICLoESzq1D9byc7KpPWAgWXdlDyqNvQnMeI6ybHRZd2Ue2SkJJOVloqzd2WSYqKxd3NHkU8vx537cdk4OBG1/gQAVg6mzVRb6fzM5/09/DkZd5KOqzuy+vzq+94/ISuBTlk5cPhHOP07GcdjyTgSQ8Kys8T/dLpYz2JMS0VZwp4eAOuAAKybNiXhxx/Ldc+cIAgVkwh6BMuSJGRZxqqE8zoelBoBTbGys2f7j9+Xuw/Tm1GmpHwulSqTGh+Ho7tHvuU01qbcN636PkfNBs1wSnQm0SWeGm92AEB3OdlctqlHU7IMWaRkp7D7xu773l8dd4FuN2MBMG6eRE58Chofe2xbeZETW/T9v4xGI5GyTEZ2Nuf+/pt5Y8bw/ZgxfPfhGObeep1ct+6+dUiShOuw18g8doy0HTuKfG9BEISiEEGPYFGZqSkolSqgfAUWGmsbnhr5LuEnjnH52OGybk4eCTeuIUkKnLwrkRwbjb3rvUNbANG3douv1sCfyDmHkWUZ19a1USgVSNYqMo7FmMv6u/uztNtShvsNJyQ2BKNc8DBV93O7cM3ORNY4oki/Rs6pENTetiidtBh1RmRj/v8tsy7cJHXvDTLPJpCTmMWpU6fY3rw5f6SlsXHXbrIAJ40GFyst7lZW5Eiw6+BBjIWszrJ/4glsmjcnYdHiQt45QRCE4hETmQWLCj9xjJrNW6FUqcu6Kfeo0aQ5Veo3YvN3s3hp5hwcPTzLukkAxF8Px8m7EmqNlptRkdRqHgiYhrOunw6lUp16WNs7cGr3duycXfCqXYdIKYG0yhnUb9seALuWXqTtj0Q2ykgKCUmSaObZDIPRwLwT87iUdIk6znXyvb91dgbXnKviVesNNEfGYZRtcejsQ06SjpQtV4n/MRSrhq7oo9Kxb1sZtZctxgw9Cb+cwag3cFp5naOqy+glU36mJFtbJKPMK0/3oGrLlub7nPpjA2uOH+P85i3U7/7Ufd8Tl6FDuDFyFJknTmDt72+Jt1kQBEH09AiWk3YzkdgrYdRq2qKsm5IvSZJ4YshwstLTOPvvrod67/vthj7so4lMXvUHrVq14kpkFHbOLgD88MlYgrt2o27NmjRq0ID1q1fRss8A1DZWpCmSWbb5d+rXr0/jxo1p925PMjMz0cfkHY5q5NYIlaTieMzxAtsSfi2NaO8GbD50juYL0ui6cQJtunTg3M0ruL3SiJwkHbsX/kXXd54hoGkTAgIC+OuHtciyTHIHa/5KOciKZSuYN28e3377LVu2bKF6jj5PwAPQoOfTOGVmcmBv4ZOU7Tp2RF3Nh4SlS0v5zguCINxBFsySk5NlQE5OTi7rplQ4RqNR/vPrmfLcoc/KGSnl+/3b/MNs+ctne8gJEdcf2j1v3rxp/vr333+X/fz85MTERNnFxUX+uF93ef+aFfLvy3+RPR3s5KiL52Wj0Sjb2VjLgDykTTN57rujZCuNWvatXVuWJEl+9Yn+ctNKDeTExERZlmX5uWefk+u515Qb+zaQmzdvLm/fvl2WZVk+FXdKdmvnJnvX9pb9/f3l5s2by+vXr5dlWZZDj8yTZ40Nlv08FfLOla/JLrbWcugIJ1mWZXnPnj1yw4YNZVmWZYM+R65cqbK8acUf8vUxe+T9E/+QKzl4yBGrQuV/V2+X69atK8/+erYcEhIif/zxx7KHh4c8+/33830f/l2wQJ7w6ady1IkThb5niatWyWfq1pPTjxwp8fsuCMLjoaif36KnRyi1i4f2s/LTDzl/YC+dX30D61IsWX4YOr3yOkgShzf8/tDumd9u6GFhYbi4OOOsknCpVIW6VSuTlJHFoUMHOXzoIOkZmVRzNV2nu3EFNxcXRr/zDu3bt2fXxSO82/YV5Lg0AObNn8fuiWv5Z+IaFixYwIABAzAajby7613ajGhD6MlQQkJCWLBgAUOHDmXv6uep/+eHOJz9FwmIzXDAxVZLfU9XANq1a2femT0x6SZx8XE89UIvnPrWprZ3dZycnNgvnyMjLR2lpCAlNQV/f3/0ej0Gg4GmTz6Z7/vQ4sUXscrOZu/vhb/3Tv37Y9W4MXGz55TqvRcEQcglgh6hVKLDLrJh1nQiL5yl5ztjqd+mQ1k3qVBqjRbXylW5dOQgRoPhod335ZdfpmrVqnzyySf88ssv+Pr6khCfQHh8InbOrmzc+Be6nBx+/242o/73P9xcXcnU52BlZ0+2szuxiTfJysoCIDoxjpPR53my51M0b96cn3/+GU11R3ThKSQlJZnv6ah1pLFPY1ytTcFMXFwEBl0KC6aupfLcbCYccWHyt5N5svc4EtMy2RdpWiq/YcOGfHdmt2vlTXgTHZeiw7kWcY30zAyeeaovv/32G5UqVWLWrFn4+/vTrkuXfN8Dja0tPio1kZmZ+Z6/k6RU4jLoJTIOHyYzJKR0b74gCAIi6BFKKT0pEQDXKj7UCWxbxq0puuBX30CXlsa+Vcsf2j1//vlnrl+/ztSpUxkzZgyOjo7MmTaZTaHnefr5F9n17148Hew4ExWHb2Uvlv04n9RMHZvOXOJ0mp62bduad0M3GI1cTrrKokET2bx5M/Pnz2fo7LcJ+qIf/fv1Z+3atSgUCvzc/TgRd4KxY8dSw8eb53v3ZP0zWj6YPZbohEymTZvOvMX7cHFxY8WLzRi/LZZmzZqxdevWfHdmb9KkCXPmzDG3JTMrk4NHDvHCCy9w48YNZv7vdS6EhrJt27YC34fUbB2ORcy27NCjB1rf2sQvWFj6/wCCIDz2ShX0zJw5E0mSGD16tPnYggUL6NixIw4ODkiSlOevzpLWCdCxY0ckScrzev311/OUuXbtGj169MDGxgYPDw8++OADcnJySvh0QlFcOX4UgKBnXijjlhRPlQaN8Avuy39/rObcgcsP9d6DBw/mn3/+ISEhgTrenrz79JMcO36c4BqVScrIJDIphe7N/KlTowbeTvZM+eQTli1bRmRkJA0bNgTA3d2drs3aYJWqxc3Nje7du1M/oCH7/reSn2fM58MPPyQ7Oxt/d3+uJF+h/+j+jBrvyeoBNrxz0JF6bT+5py0dfTRsfzuYo0eP8tVXX+W7M/vx48fztCUjO5Pdh/5l8ODBKBQK3vt2Ls8PHsyuXbsKfP4khQI3h6INgUpKJc4vv0zazp1knipeokRBEIS7lTjoOXz4MPPnz8fPzy/P8YyMDLp168ZHH31ksTpzDRs2jKioKPPr888/N58zGAz06NGD7Oxs9u/fz08//cTSpUv59NNPi90OoXCyLJOZlsqJbZsA8K5dt4xbVHz27tUB2L4klKw0/QO7T1JSEpGRkchGI1eOH2HG+6NxdnLCxcWFa1fDsXV2QZIkNh0LxdXWltQsHW/P+YFm7TtyLTGZ9z8ez8CBA7G1taVTp04AtG/fnoOxZ7CTnEi4GsWuXbto0qIpKndr2ng1ITU1ldDQUPzcTf8vvbrlVQ571aZZFS1p6XpCQ0MBWL9+Pa6urri4uBCdFAc2ppVjU6ZMyXdndoCFCxeiQknK2ViSdKl4unmwefNmANLT0/nnn39o1KhRvu9FemwsmVot7t757y2WH6c+fdDUqkXsl18W850XBEHIq0R5etLS0hg4cCALFy5k6tSpec7l9tDc7y+94taZy8bGBi+v/Dex3Lp1K2fOnGH79u14enoSEBDAlClTGDNmDBMnTkSj0RSrPULBzu7bzaZvvkB5a+ij9/vjcSggi3B5lqPLvvWVku1Lz9DjDT8kheX3C0tOTqZ/v34kREeiS0/H3tqKl5r7k5maws8bNnH42DGm/rEVx5wsRjwRiLXGlONo6+kL7L50DaVSSWpqKuvWrTPvZ9arVy9+X7GaTj8OQl4h0bVXDwYMGEDibxc4uGc/sbGx1KxZE1srW9zT3QloEMDUNlPZtq0N4VEnGDRoEGq1Gnd3dzZu3Ag5MpP/uca/v0Zh+GwHQUFB9+zMvnz5cmRZpn79+rzx3GuciDclSxw7/F0WLVrEnDlzyM7OplevXjz//PP5vheRt4Itrzq+RX7/JLUa95FvEPHue6QfOIBtUFCJ/jsIgiCUKOgZOXIkPXr0IDg4uMAA5UHUuXz5cpYtW4aXlxc9e/bkk08+wcbGlJr/wIEDNG7cGE/P2wnnunbtyogRIzh9+jRNmjS5pz6dTodOpzN/n5KSYpFneZRlpqawdf43ABhycqjXpgO1WwSWcatKRp9tCno6D2nEzp8vc2zrVZp1q27x+3i6ujK8lR8ZyVUJHjYKn4Z+LHzzVcKOHmLe99+zcsKHDPrsG66fPsmun380Xzdy6BB0f25l9OjR9OnTh6lTpzJv3jzi4uI4deoUVlZW/PnCt+RU1jP6z9k0atQIRTZoMyRWr1iFs7MzGRkZZC3PYmvyVnaqdmKrT2Dr/3zo9P2ZPG3MuZnFwp5WGJqNQd3rvXue4e6d2Zd+Pp9qsjVP9e2Oc2V3RkwYXaT3IiYsDGQZrwJ6ggpi37Ur2roLSFz6kwh6BEEosWIHPStXruTYsWMcPmy5VP5FqfPFF1+kWrVqVKpUiZMnTzJmzBjOnz/P77eWvkZHR+cJeADz99HR+W8yOWPGDCZNmmShp3h0pd1M5Mifa8lKSyc7KwOVSk3Pd8ayd/lS2jw3qKybd1/LPppJTNhBGnV6AVtnJ6zsbLCyscbK1obECNPPRa0m3iTHGTj0x2W8azlSydfZom3Y9fOPZGdmMPTreTh6mHoqvWvVIXTnVrr+z7Rruj4ri2Y9+hAddpFz+3YT9MyLtOjZj17v3R4mHj9+POPHjwfAkJ3DmVl/4ZzkQrq3jn379pnqicsg5qujuNUwzf2xsbFh/779txtzej38NhhSo8H+dq+p4WYyKkmH0TH/LTDulpGdhZu9C151qhbrvYiLicUuS4fWvmh7sxnS0gEZpZ0drq8MJXLMWDJDQ7Fu3LhY9xUEQYBiBj3Xr1/n7bffZtu2bVhZWVmkAUWtc/jw4eavGzdujLe3N507dyYsLIxatWqV6N7jxo3j3XffNX+fkpJC1arF+yX+qEu4cY2Vn35IVnqa+VjwayOp2aQFNZuUz8zLd4oJOwjkcGrnLwWUUKLSqmn5dA2iLiWzddEZnh/fEis7y2yjkRgZwZk9O3li6HBzwAPQpHsvNn3zBQk3rgGmwBLgqZHvEvzaG2htbAusMysihfi5J3DGhbS6WdQbfDsnjqQ0TdPLjkrHqq7LvRdXaW7698YRqP+0+bAx2XR/Di2EDi8X+lwZOZnY3aeNBUlIT8NZKvoQYviwYeiOH6f+qVAcnn6a+AULiZvzDT4/itVcgiAUX7GCnqNHjxIbG0vTpk3NxwwGA3v27OHbb79Fp9OhLOJS1NLW2apVKwAuXbpErVq18PLy4r///stTJibGtAFjQfOAtFotWq22WO19nGRnZvDrpx9g5+zKy198S/Tli1jZ2FKlQcX5K1tt5YajZ3UGjH+HjJR0stIyyErLJCstnayMTBzcnFEoTIHCk680YOXU/9j+0xl6jLDM/J7/1q/GxsmJxk/kzVtTp1Vr9rq68+fXMwGwdTb1LimUyvsGPAAJ/4UBoGsM9QbeDnh0V1NI+Pk0SlcrbPxMPTZLlizhlVdeYd26dfTp04fYLDUv/5pD2MLBaJ29+f7772nfvj3aBrWZOsaeX47t5+JYBWvWrME50xp7RwcatgsgVZfOyy+/TFhYGFqtllbNW9Ky7r1DxoXJlmUc1Pn/2jFkZxNx7BjXQ0OJiIggJiODm7Vr4aNR4/nLMlyHDsH9zTeJGD2ajCNHsGnevNj3FwTh8VasoKdz587mVR+5hg4dSr169RgzZkyxA57S1BlyK1mZt7c3AEFBQUybNo3Y2Fg8PEyTardt24aDg4N52a1QPLHhl9Glp/PchJnYu7ph7+pW1k0qNoMhC62NLTYOptf92Dlb8eTQhmz87gRHN4fTvHuNUt37yJ5dnDhymCcHvIDqron0SpWaDoNeZePsmdi5uFK5btF/RiUvNTJgWzfv5PHkjZdR2Khxf90fpa2a8PBwFi5cSGDg7TlXY8eNI7BxTTY/XY3Dtd+jb++nuPLTKNRPf0aH5h14rv6/DDtRg8zkdE5ePQnApsPb2fbXVurW8uW3A6sIORFCj749Gd5vSLHfE0e1hqRbc6mSb9zg4p69XL10kZj0dBLUagwqFcgyTllZeGi1yNbWZFeuTNzcuTh064p9lyexatiQ6GnTqbF2DZJCpBoTBKHoihX02Nvb37MU1dbWFldXV/Px6OhooqOjuXTpEgChoaHY29vj4+ODi4upu71z58707duXUaNGFanOsLAwVqxYQffu3XF1deXkyZO8cysdf+7y9i5dutCgQQMGDRrE559/TnR0NOPHj2fkyJGiN6eE4q5eQalS4VK54g75GXMysbIr2vwRgGqNXGnRvTqH/ryCR3UHfBq4lui+ly5dYuPOXUje1Qno0j3fMnWD2lKjyW9orKyLVbdCpcIIyDl5s0nLRhltTUeUtmqMRiOvvfYac+fO5b33bk9MXr16NZeWfwCn5tMi4yMqWWWxe/duOj2RSkvXLBQ2pl7RzJR0AHq17ErKzWSmz5xOqw5BzJo7Gw+1E/b29pwNv0Ar2her7S6ODlxOTmb2mDEkWZue2y4rC0+Vijru7lStV49qLVtidavna82aNaQlJ6OwtSFm+gyqzP0Gz3FjufrSINL37cOuXbti3V8QhMdbiVZv3c+8efPyTA5u3970S3HJkiUMGTIEMAUx8fHxRa5To9Gwfft2Zs+eTXp6OlWrVqV///7mSZ0ASqWSjRs3MmLECIKCgrC1tWXw4MFMnjzZMg/2GIq7egWXKj7mpekVjT5bD7IO6yImwsvVokcNYsJT2LboDAPGNcfBrXhBSUREBL/++isAysw0FPfpAS1uwAOgUJvqM+rzJt6UlBJyjgzArFmzaNOmDc2aNTOfT0hIQK/X4+XXCY7PAn0m1Su5cy0+Df33L6FN34VeacrLk5lq2iaiWqOaYKtCBsa/9xGn/j3GubALuDq5kGYofCuJu/k2a8aFLVvw0GoJrFYN37ZtcfUtePm6QqHAKEl4jh1L5Hvvk7Z7N7bt26NtUJ+ERYtF0CMIQrGU+tPs7nw8EydOZOLEife9Jjw8vFh1Vq1ald27dxfalmrVqrFp06ZCywmF02fruBp6Ap9G+SeKrAiiw0xzujJSijcEIikknhzakN9mHmb918fp806TIgc+Op2ONWvW4OHhQVJ0JFaFDAnqsjJJSkgg5eZNUpNvkpacQlpaGrrMDFy1GgxZGWSlp5GVlkpWmulffVwG3asMu6enB6UC2WDk1KlTrF27lj179uR/0+iTEPgGtP8AfmsFRgPKdNMSdqNrC+AcmenpoAUHD2dS0lMBcKrkQttng2lLMFuP78LG0fbeOUOxsXnm/uTOGQKIjY1l1Oef5zmXG/AUdJ1CoUCWZRy6dyd57Vqip0yl5p8bcBv+PyJGjyZt927sOpT//d4EQSgfKuaf8MIDd+j31aTfTKBFr/5l3ZQSy0wxfVh71/YspOS9rOzU9Hm3KX/MPs7vXx6j11sBuFQqfLXS1q1bSU9PZ9CgQSz+dg7JaeksnjOLrCwduuxs9IYccgwyOYBRUsB95qQ43YzGVq3C1sYGazs7nL0rY2Vnh7XCDk6Bg1veCfqSSoIcI3v37iU8PBzfWwFFdHQ0w4cPZ9KkSahUKqLXjsXLTgGt3yT8RhQ+gfXBmEqaqje2w7+F1cFkZmSi1VqjttLgauVqui462rwoIDw8HI1Gw/fff593ztDYsQQGBrJ582YOHz5M3759uXLlCmq1ukTnJEnCaDQiSRJen37K5V69iZ83H/fRb2PbOojoadOp1bo1ktoyq+0EQXi0iaBHMNNlpLP+8yko1Wqunz5JYL/ncalUpaybVWJuPu6ARMKNq0Dxh0HsXazo+15TNswJ4bcZh2kzwJeG7SqZsyLn59KlSzRv3hwXFxdcnZxI1ycQEZ+ISgK1UomVRoNWo8XaSou1tQ02drbY2tlhZ++AvaMjjs4u2Do58dWsr2nS7wX++ecfOnfuTNu2bc33NSTriDr1H2pN3rlqklKBbJAZMWIEI0aMMB/v2LGjOcHhoUOHmHfkVyZ2tOLwJ0FE3NTR4a0fYNELqHPCQG9K1pmVlYWT4na+ogEDBjBv3jwmTpzI4cOHiYiIYPHixfnPGbo1n69FixZUqlSJ3bt3ExwcXKJzCoUCo9EIgKZ6dVyHDSN+wQIce/XEY8wYrvTuQ/KGDTj1r7jBuSAID48IegQAdBkZbJg1gxtnTwHQ6IkutOwzoIxbVTou3m7YudYj7Mhu4KUS1WHrqOWZMc3Zt/YSu1ec58KhaBp3rELVBi5Y2ebtXcjJySElJcU8YX/oW++U6J6ybJqXk5uCYceOHWRmZtKly61l70pT8JN+MAo524hCq0TSKNDHZqB2v/8w3Geffcag1svxnZuGRmtg2YqVqH2akx08m88/fZb5c1yJy5I5evgoKpWKF0cMxt3d3XTdoEH4+vqi0Wjo2bMnnp6e+c8ZuiNFRPXq1bl27VqJz3l6eprfDwDX4cNI3vgn0ZMm4/PTUuy7dCH+h3k49OiBwkK5wwRBeHSJoEcA4Ohf67gWGkKDdk/Q+tmXcPQo/pBQedToiSd55c2BfFd7N9Z2Ntjb2/PNN9/QpEkTNm/ezPjx48nOzsbGxob58+fj7+8PmDa/HT16NGlpaUiSxKxZs3j6TX+Obb7KoJde5tyNozjau6AjhRbNW7Jg4XxOnzbtAl7aBJeSJKFQKEhPT8fLy8scTOVSWKlQOmnJOptI1tnEPNdqKtvdU9+dc+Q8PT3Z+np1qNcdnvoCVKal9Jp2Pfhwyu98vG0wBuu6LMvphdbGBXd399vXbd0KwKlTpxg2bFjBc4YsKHd4K5dCq8Xrk0+5/tprpPz5J26jRnKlV29S/tqEU/9+D7w9giBUbCLoEZCNRs7v30u9Nh14atS9+y5VZC16deTllW2pVjeQl6aPYd26dQwZMoRdu3YxcOBA9uzZQ8OGDdm7dy8DBw7k1KlTyLJM3759Wbp0KcHBwVy4cIHg4GDOnz9P3/ea8tthDzp1f4suLZ9l1/mVAMyfPx+ADh063LMdSknk5qcaOHAg69evx2C4PWlZUinwHtsS2SBjzMpBzjaYXnojKpci9HYolGDvbQ54cmnbdkXHL6i3vUxnaSEnNB/me3mhc4bumvvj4+ODq2v+84IKO5eTk5Mn6AGwa9sGh+5PETPzM2r9vQnb1kHcXLECx3597zv0KAiCIDJ7CVz8bz+JkTcI6NKjRNcvWbIESZJYv349ANOnT6du3booFArzsVxDhw6lTp06+Pv706ZNmzz7rX388cc0btyYgIAAAgICWLlyZUkfyUyj1VCzYTtirxxGl6EjOTkZSZIICwvD1dWVhg1Ne1S1a9eOa9eucezYMRISEoiLiyM4OBiAOnXq4OTkxN9//22q00qJRzUHOr5QD4WsxreyH0OGDGHIkCF07Nix1G0GU5oGPz8/7O3tkSQpzxBPLkkpobRVo3K2Qu1pi6aKPQqbIkzolRRgNOR7Stv2SdI7/YQ7iTSJ/wxDeuI9ZUaMGEFUVBTh4eGEh4cTGBjIggULGDFihHnuD2Ce+9Ph1uqqkpzLXb11N48xY5F1OmK//hrX/71O1unTpPwlVm4KgnB/IuipQPS6LM4f2EtGcpLF6pSNRg6s+ZVqfk2oXK/4mavzy/obHBzM33//bV6qfKe+ffty5swZTpw4wbhx4xgw4Pa8oQ8++IDQ0FBCQkL466+/GD58eLHyORWkVd8erDhwkKpVq/DJJ5/wyy+/4OvrS0JCAvv3mzbj3LBhA6mpqYSHh+Pm5oa3tzerV68GTB/C58+fz5NqYc6cOfj5+bH811+4ev0q1atX559//skT6BUW/Pn5+REQEECLFi3YsWOH+VzHjh2ZO3cuEyZMICAggC1bttzT21EqkhLkguuz79CFkw0n40ISyXM7ok+JLXLVn332Gfv378fX15chQ4awbNky1LdWVpXk3N3DW7nUnh64v/02SatWo7C2wv7JYGK/+gpjZvFzBwmC8PgQw1sVyK6ff+Tk9s1obW0Jfm0k9VoXLxtufi7+t5/461cJHjaq2NcWlPW3ZcuWBV7Tq1cv89eBgYFERESQk5ODSqXCycnJfC4tLQ1Zli3yYV+zSV2GdHoapcYam6AAxowZw6ZNm1izZg3jxo0jLS2NoKAgGjRogOpWIsY//viDMWPGMGPGDBo2bEjbtm3N56ZNm4a3tzcKhYIXnnuJz+dMpvdzXfIN/p5//nleeeWVe9r09ddfm5/3+PHjdO7cmfj4ePM+YN988w19+vRhy5YtHDt2LN/ejhKTJJDz7+nJ1WrA61yuXAnPrcNJmdOG6y0+oUZQbxwdHe8pe8+coVtzf+5WknN3rt66m/OLL5C0fh1REydS+cuvuNy7NwmLF+M+cuR9n00QhMeXCHoqkMjzZ6nVvBUqtYa/5nxOckw0LfsMKNE8huysTJJjY9ixeB7VA5pRuW79YteRX9bf4pgzZw7du3c3BxNg+rD/7rvvuHHjBj/++KN5H7XS8g3sxKkdPxNg34t//vmHhIQEnnjiCZ544gnAlFTQy8vLvE+bv78/mzdvNl9fv35981BY5cqVzccb1mvEP//s4JVXXuH7778vcvB3Z4CXnJxcYLnTp0/j6uqaJ5gqNcX9e3py1WzdizhnV9xXdSfx0Nd8ffAy1atXp3HjxjRo0ABr6+Jnky52UwsY3gKQVCq8J04k/LnnSf93Ly4vvkjikqU4P/ccKreKt0+cIAgPnhjeqiASblwj/vpVGrbvTI+3PySw/wv8u/Jn1n8+mYyUez80ZVlGvusv5NSEeOKuXmHH4nnMHTyAnz8YhdbGhm4jRhe7PblZf+/cCqQ4li1bxurVq1mwYEGe42+99Rbnz59n//79TJ8+nYSEhBLVnyspKYnIyEiCX+mHxqYyPy2YjaurKy4uLkRFRZnLTZkyhU6dOlG7tmkbhjvPLVy4EFtbWzp16gTAjRs3zOdu3LjOzeSbdOzYsdjB39ixY6lVqxb9+vVj7dq15l6e3HONGzfml19+wdramlq1apXo+fN1nzk9d3O3NQWk1Xq8R+/evZEkiY0bNzJnzhwOHTpk2R6ofBQ0vJXL2s8Pp+efI27ON6aJzEolsV988UDbJAhCxSV6eiqIM3t2YmVrR42mLZAkiTbPDsS7dh02f/81P3/4Jk8MHkadwNsJ7LbOn0t4yBH8u/Qg8vwZ7N3cObn9ds+Fc6Uq+DTyp/3AISXa/6mgFTxRUVF5EuPlZ9WqVUyaNIkdO3YUuNLJ39+fypUrs2vXLvqXIvFccnIyAwYMIDMzk+SYOKyVSjb+/ReSJPHpp5+yd+9ecnJyCAoKYtGiRebrFixYwPLly5Flmfr167Nu3TrzeztkyBBiYmJQKpVER8fg6uzG1KlTi922mTNnMnPmTLZv386HH37Ivn370Gg0/PLLL1StWhVZlnnuued47733UKvVNGzYkMaNG5f4vTCTlFCUYEWWYdun4NkYTdMXaaJQ0KRJE1JSUti9ezd///03169fp3fv3ua5OZZ2v+GtXB7vvEPq1m3Ez5uHxwfvEzX+E5xfeAHrgIAH0iZBECouEfRUAEaDgdO7d1C3TQdUd3y41Gzagpc/n8v2RT+wcfZn+DTaTPuBQ4kNv8ypf7aiUKrYt3oZSpUKg16fp86e74zF3ad6idt0v6y/97N69WrGjx/P9u3b8fHxyXPuzJkz5uGlsLAwjh8/bv6+pKpVq2ZO8rf0g+kkRV8k4NaH4cKFCwu8bsKECUyYMCHfc9u3bzd/3btHf3bv3Vmi4C9XcHAwo0aNIjQ0lGbNmpnz/EiSxDfffIOPjw9XrlwhIiLCQkGPotA5PQCc2wjXD8JLv+fZLsPBwYGePXtSo0YN1q9fj9Fo5JlnnsnTU2Up9xveyqV0cMBzzIdEfjgGpz590NatS/S06VRftRLpAbRJEISKSwQ9FcClIwdJT7qJX+eu95yzc3GlzwfjuXL8CDuXzmfZuNEA+LZsTc93x5Gjzyb95k2WvPM69m5uDP7yO1Lj4x7o9hJTp05l3rx5xMXFcerUKUaNGsXx48dxd3dn4MCBeHl50bt3b3P5HTt24Orqyocffmjei0mlUvHtt99Sv37x5xoVxGjItviHYFDLNvQI7svwd0wZn4sS/On1eq5evWoeSvvvv/+IjY2lZs2a5OTkkJCQYO4B27dvH97e3vj7+xe6UW+RKZSFD2/l6GDreKjVGWp3zrdIo0aNUCqVrFq1ih07dvDkk09apn13KGx4K5dDz54krf2d6GnT8fr0VvLCjRtxvGPivCAIggh6yrk9K5Zy+I81VKpTH4/qNQssV6NJcwY38uf0rm1kpqbS/GlToja1RouTpxevL/gFjZUVSpX6gQQ8d67gGT9+fIFzffR39TjdaePGjZZuVh7J0eexcS5dtuS7SSgwFhBAFBT82draMnjwYJKTk1GpVNja2rJmzRqcnZ1JT0+nR48e6HQ6FAoFbm5ubNiwgbCwMKwstc2CJBU+kfngD5B0HV5Ydd9i9evXp0uXLmzduhUXF5cST2ovSFGGt8AUHHlN+JTLvfuQeewY9k8+Seysr7Hr1BmlXeEbxQqC8HgQQU85djMqgiN//k7znv0I6v98oeVVajX+T3bP95y1nb2lm1ehJEbGYTSk4uDmbdF6TT0Rt4dfihr87du3L9/jtra2HDly5J7jV65csdyk4ULy9JAWC3u+hBavgUe9QqsLCgri5s2bbNy4EUdHR3MPliUUNegB0Nasieurr5CwcCFVvv+O9H37iP3qS7wLGKYUBOHxIwa8y7H9v63A1smZNs++hMbapqybU6Ed22zaJ6r5090sWq8kKZCLsPy7tBQKRZ6tKEpFbQMhK2BJd9gxGS5shcyk2+d3TjENgXUcW6TqJEmiW7du1K5dm9WrVxMZGWmZdlK0OT13cnv9dVSeniQuWYLr66+TtGYtustXLNYeQRAqNhH0lFPx18I5t38Pgf2eR6XRFH6BcF/O3qY9nU5s240hx0LBA7d6eh5S0GOxrMx9vofgCWDjAsd+hhUD4LPqMNHR9Dr2Mzzxkel8ESmVSgYMGICbmxvLly8nMfHe7StKoqhzenIprKzw+mQ86fsPoHJ1Re3pSczUKQ98ab0gCBWDCHrKqX2rl+Ho4UmjJyw/OfRx1OypNlg51ODqiY38PHa6xepVSIoHGvRcuHCBtWvXcuHCBcsFPS41IGgkPLcM3r8Ibx6D3t9C/Z7g2QhavwXN780iXRiNRsPAgQPRarUsW7aMtLS0Ujc1d0VYcZ7drkMH7Lt0IXb217i//Tbp+w+QumVLqdsiCELFJ4Kecig1IZ5Lhw/SsvczKFVi2pWl/O/7r3H1CSIp8rTF6pQkBbLxwfUiHDx4kMuXTZmQczfntChJAtda0OQlUxA0Yh90mQLKkuXdsbW1ZdCgQeh0OlasWIFOpytV83KDnuL21Hh+NA45PYPMkBDsOnUiZsZMDGnppWqLIAgVnwh6yqGroSEA1G4RVLYNecSo1CoUSiVGQxrZumyL1Kl4wMNbkiTh4+NDv379zNtglHfOzs689NJLxMfH89tvv5VqLlJuQsji9nKpvbxwe/NNbv76K469e2NITib+++9L3A5BEB4NIugph+KumiZePu4rrh6E6n5+AHw79GWSYm+Wuj5JUj7QicwWncvzEHl7e/Pcc89x+fJlNmzYUOI5NSUZ3srlMugltHXqkLBgAa7DXiPx55/RXbxYonYIgvBoEEFPOWTn4gqQ755aQum0f7E7Pn5PIRvSOLvvWKnrUyikBx70VNRJuLVq1aJv376cOHGCHTt2lKiOkg5vQe6GpBPIOnMGhVaLpnJloieLSc2C8DgTQU85dGLrX2XdhEda/3EjkCQrrp08Veq6TBOZH9yHaEXt6cnVuHFjunTpwr///svBgweLfX1Jh7dyWQcE4PzCC8R9/wMur75CxuHDpDzgJJiCIJRfIugpJ1IT40mMjCDs6H8kx8YAYOvkXMatejQpFArcazTnxpl/SIop3dJqSfFg8/QUd8l2edS6dWtat27N5s2bCQ0NLda1pRneyuX+7jsoHRxI3b4d+65difnscwypqSWuTxCEikssDSpDCTeuc2Dtr1Rt0IjtP+adZNn19bfLqFWPh67/G8wvY/5l8dvDUahsUWlt0VjbYW3vjo9/TzRaNSqNApVGiVqrNH2tNn2t1t4+jlFCxojBYECpVFq8nRW9pydXcHAw6enprFu3Dmtr6yJnbS7N8FYupZ0dXhM+5caIN3B/913S9u4lbu5cvD76qMR1CoJQMYmgpwwtfc+0C/f5/XvMx5r16E3znv2xcy56Yjih+DyqexM04G2iwy6TlZZCVloqGSmxpMadQaEOwGi0JSfbSE62AX22scBl6Wn2OrJsU5gyZQpKpRKtVotGo8nzKukxtVpdoef03EmhUNCrVy8yMjJYtWoVQ4cOpVKlSoVeV9rhrVz2TzyBw9NPk7BgAU59enNz2XKc+vXDql7h22wIgvDoEEFPGcmdpOzg7mHa+TwhHidPb5GX5yFq/UzexI/hJ4+zdton9HzLHwc3jzznDDlG9DoDOdkGcrJNX+t1OaSm1CElOw6VFWRnZ5OdnY1OpzN/nftKS0vL871OpyMnJ6fQNkqSRNWqlt0ktazkZm1eunQpv/76K8OGDcPBweG+11hieCuX14RPuXL8OJmhp9D4+BA9aTLVli9DUohRfkF4XIhP2DKSEhcLQM2mLdFYWeNa+dH4YKvIFArT8JTRcO8HrFKlQKlSgO3dSfucgZLtWm80Gu8JhO4OlnQ6HZUrVy5R/eWRRqPhhRdeYOHChaxYsYKhQ4ei1WoLLG+J4a1cSnt7Kn3xOVdfGoRN06ZkHDlC8vo/cOrXt9R1C4JQMYig5yEzGgwsfe8NbkZFANCoY3AZt+jxJcsyMWEXuRkThVfN2iiUt3oVLLWxZyEUCgVWVlZYWVk9lPuVF/b29rz44ossXryY3377DT8/P9RqtXlI787hvdzeMEvNa7Jp2hT3N0cRO+cblFV9iP3yS+w7d0Lp6GiR+gVBKN9E0PMQHf7zdw78tgK9LguAQZ99g0f1mmXcqsdPjl7Pye2bObFtE4kR1wGwd3Wn+6j3AJCNDyfoeZx5eXkxYMAAfvvtNy5dulRo+dy5PZZg/+STfLn1Ar/X7sCarZOJnT0b7wkTLFa/IAjllwh6HhJZljm26Q/sXd14cvgovGrVEbunl4Hk2Bj+/HoGseGX8W3Vhk5D/4dKrWHlhA+5dPQQ8PB6eh53vr6+jBs3Dr1ej16vJzs7O99/FQoFLi6Wm9ifcewYf9UIIlupZmODzvRduQrn554Tk5oF4TEggp6H5O9vvyItMYGArk9TpX6jsm7OYyn8xDH++uYLtDY2DJw2C8+at5dN+zTy4/jffwIi6HmYJEkyD2fZ2to+lHvePPgfmcp2AKz0bsllyZYPDh2nrgh6BOGRJ4Keh+BmdCRn/90FwIltm7hw8N/b3fW3/s3TeX/XOdP53GN3FpO4++DtQ/e5VjbNkTAaDcgGA0aj8da/BowGI8ZbXzt6ePLqnIUWHVooC/qsLI78tY79v62ghn9Tnnrz/Xv2NWvavQ/XTp0EwCiGtx5pxw6eJqf9E/zQ3JoRR2C7Tws6O7lSt6wbJgjCAyeCnofA0d2TOkHtcHT3QGNljcytlSi5/+RZmSLfOsY9x+48ePvLO4/lU+7eCwBQKJVICgUKhfL218rbX8eEXeL07u0YDYYKs4w+OzODM3t3kRwbTUpsDCnxsSTHxZKZkgySRFD/5wnq/0K+S5RrNmlu/jq/1VvCoyEnPp6zLtWxytER3PNJ/mkcxRNLTpHh6lnWTRME4SGoGJ9mFZxCqaTn6DFl3YxiOfvvLlPQk5NTYYKeS0cOsWPR9zh6euHo7ombT3VqNmuJg5sHHtVr4l6tRoHXSgoFHQa9yu5fFplXcQmPnoxjxzjjWp36mbGotRpq1K1GE58bhN5IKuumCYLwEFSMTzPhocsNdAyGHO7OTFNe5Q7CDf7iW9Ta4i8Db9ajD9X9m4qcSY+wjCPHOONSg/6u2eZjfpUd2XspvgxbJQiPB1mWy3y6hPiTVsiXQmkKeoxFyBpcXijVpvDMoC9ZmyVJwq1qNZGh9xF2eusekrV2tKx/O6GkXxUnLsel88uB8LJrmCBUIGm6HN5dFcLVhPQiXzNxw2k6f7X7AbaqaMRvdyFfd/b0VBTmoCdHX8YtEcojY2YmpzWuSLKRVu38zcd7+lfiaT9vJm88w6VYsfu6IBTmbFQKvx+PYMKG00Uqv/V0NEv3h3M5vuhB0oMigh4hX7d7eirOSialKrenRwQ9wr0yQ0M541KdaikxOHvczvujUSn4coA/VZxt+GjdKYwFbC4rCI+bJUuWIEkS69evByA2NpZu3brRp0NzIhe9webt//DNjovsD4tn46GztO/0JNVq1KJmnfp8sXQdqw5f48st5+ndpSORS94kasmbNGrUCEmSOHnyZJk8k5jTI+RLoTLtQ2WogMNbOSLoEfKReew4p11q0Mzp3nNWaiXT+jRi4KJDfLb5HOO613/o7ROE8iQ8PJyFCxcSGBhoPjZ27FgCAwMZNu1HRn3zG+l/zWSuTwP0soL4TbNRObjj9NxoMqMuMO7NYVR+fRGuDjaMnruaOp72TPrzDBOaZTB58mT8/PzK5LlK1dMzc+ZMJEli9OjR5mMLFiygY8eOODg4IEkSSUlJpa4zMTGRN998k7p162JtbY2Pjw9vvfUWycnJea6VJOme18qVK0vxhI+v3OEtY0Ua3lKJ4S2hYJG793HdwZPmNd3yPd+6thuf9GjA/D2X+VnM7xEeY0ajkddee425c+fm2RB49erVvP7666Rk6XGoWo96NavxZTs1uz/oiBx2gGVfjmf9yDbs/2o4fnVrsCDYmmOfPMmMfn7YW5l+P/+4aBGvvvpqWT1ayXt6Dh8+zPz58++J1jIyMujWrRvdunVj3LhxFqkzMjKSyMhIvvzySxo0aMDVq1d5/fXXiYyMZM2aNXnKLlmyhG7dupm/d3JyKt6DCcDt4a2K2NMjhreEu8lGI8cj06AaBLYquBfnlbY1iEjKZOKG03g5WNGloddDbKUglA+zZs2iTZs2NGvWzHwsISEBvV6Pl5cX0cfO4mitpnr16iRER2JHFjk5ep4ObGAuX7tmDSIibpi/VyslclLi2LtnD8uXLXuoz3OnEgU9aWlpDBw4kIULFzJ16tQ853J7aHbt2mWxOhs1asTatWvN39eqVYtp06bx0ksvkZOTg+qOPDJOTk54eYlfVKVVEXt6VCLoEQqQHRbGGZfqOGelULNhrfuW/bh7faKSM3lr5XFWDAukqY/zQ2qlIJS9U6dOsXbtWvbs2ZPv+diULJYfuka/ppU5d6Lo9aoUCtJCt9P1qe64ueXf2/owlGh4a+TIkfTo0YPg4GCLNaS4dSYnJ+Pg4JAn4Mmtx83NjZYtW7J48eK7sh3npdPpSElJyfMSTCpkT48Y3hIKkH7wEBecq9JQSkNRSEoChUJi1rMBNKrkyGs/HSEiKfMhtVIQyt7evXsJDw/H19eX6tWrc/DgQYYPH86vK1eiVKkYsXAnVmoF7z1Zl/DwcHx8fHB1dUWlUhEdHW2uJ/fcuegUPv3jFAv2hJEeup0XBw0pu4ejBEHPypUrOXbsGDNmzLBYI4pbZ3x8PFOmTGH48OF5jk+ePJnVq1ezbds2+vfvzxtvvMHcuXMLrGfGjBk4OjqaX1WriqR0ucw9PRVp9Zbo6REKEDNtGtkKFfYaZZHKW6mVLHy5ORqlggl/FG1ZriA8CkaMGEFUVBTh4eGEh4cTGBjI57O/ZW1mQxQ1g9j75wpmPRvAhdMhRERE0KFDBwAGDBjAvHnzANNUlYiICKJsavD0N/+y7UwMumsnsFFL9H36qbJ8vOINb12/fp23336bbdu2YWVV/Iy3lqgzJSWFHj160KBBAyZOnJjn3CeffGL+ukmTJqSnp/PFF1/w1ltv5VvXuHHjePfdd/PULQIfk9zVWxVpeOt2np6K02bh4VEgk1OM1ejOthom9GzAiOXH2Ho6WszvER5byw5eJcXdme9mf8lPMz7g1R6t0Wg0LFu2DPWt37ufffYZgwYNwtfXF41Gw8hJs/lkwzleCvTh06cbMuTlRYx+YzgqVdH+8HhQihX0HD16lNjYWJo2bWo+ZjAY2LNnD99++y06nQ6lsngPVJw6U1NT6datG/b29qxbt878ZhekVatWTJkyBZ1Ol2cGei6tVpvvcUEMbwmPFpW7OwrZiPE+w9356dbIi4513Zm44TRtarthqxVZPoTHy/RFa3hx4SHm9W1Et0beDO68Ld9ynp6ebN26FYCjV2/ywsKD9G3izZTeprw8K1aseJjNLlCxhrc6d+5MaGgoISEh5lfz5s0ZOHAgISEhxQ54ilNnSkoKXbp0QaPRsGHDhiL1CoWEhODs7CwCmxKoiBOZDfrsW/+KoOdxEJGUyfj1oaTpivAzqlCgNBoxFDPvoCRJTO7ViIT0bL7ZcbFkDRWECuzHvVeo52VP1yL2dF5LyGDYz0cIqOLEzP6Ny3yvrbsV688We3t7GjVqlOeYra0trq6u5uPR0dFER0dz6dIlAEJDQ7G3t8fHxwcXF1MW1M6dO9O3b19GjRpVpDpzA56MjAyWLVuWZ9Kxu7s7SqWSP//8k5iYGAIDA7GysmLbtm1Mnz6d999/vwRvi1DRenrirl7hj6+mYWVrh1ftOmXdHOEhOBeVwrKD1zgSfpO/3mqHUnGfX64Kxa2enuLfx8fVhjc71Wb29ov0bVqZel4OJW+0IFQgSRnZHL16k94BlQoNXoxGmb2X4vl4XSgOVirmD2qGtoyHsvJj8W0o5s2bR5MmTRg2bBgA7du3p0mTJmzYsMFcJiwsjPj4ou9qfOzYMQ4dOkRoaCi1a9fG29vb/Lp+/ToAarWa7777jqCgIAICApg/fz6zZs1iwoQJln3Ax8Ttnp7yP5H5zN5/WDH+fTTWNgycMRuXSlUKv0io8Hw97AE4F53KzL/P3r+wVPw5PXca1r4m1VxtGC+2qRAeE/tPhPNz71fRp6TwXIv7z3VdcegajSduYfDi/3C317J8WCDOtpqH1NLiKfUA9d35eCZOnHjPBOO7hYeHF6vOjh073nfpOWBOiChYxu1tKMrvUJEhR8+unxcRsmUjDTt0pvNrb6DWiKHMx8WhKwkA9G1SmYV7r1DdzZaBrarlW1aSFChL2NMDoFUpmdKnES8uPMRvR6/T1tcdbwcrFPfrXRKECsx+2UKevH6Eev170LCS433Lhly/SXq2gdnPBRSpV6gsiVl5Qr4UCiWSpCi3S9bTEhP48+uZRIddJPi1N/ALfqpc/48mWFbI9SQ+WHOSwJoufDnAHwcrFZ/+cRofFxva+brfe4EkoTAaKU0I37qWG/2aVGbM2lDAFGx9/VxAKWoUhPJHHxFB7Jw5qP78EwBnT1dyDEb+OR9HdHIm2QYZvcGIPseI3mAk2yAjYfrdG5GUWe5/D4ugRyiQQqUslxOZr58JZePsz1AolTw3cSaV6tQr6yYJD9mV+DQAFg1ugVIh8cnTDbiamMEby47x+xut8fW0z3uBwtTTk1XKkamPetTn9+MRAKw7HiGCHuGRIev1xH33HYmLl6BwcMDp/Q9I/PJLEhYv5vjEcfxerxs7ardBrZRQqxSolQo0SoXpe6WCGm62VHe1LevHKJQIeoQCKVWqcjWRWZZljm36g93LFlOlXkOeHj0GG0ensm6WUAYUd/01qVIqmPtCEwbMO8DQpYdZP7INbna3hzolSTJNZC7lfd3stOz98Al+2h/Oj/9eIV2XI5axCxVe4q4/iXn9QwDc3hiB66uvorC1JWLud1S7GALAqGoyP0wr28SClmDxiczCo0OhVJWbiczZWZlsnPM5u37+kWY9+vDM+Kki4HmMpWTloFJIaFS3f4XZW6lZNKQFWXojw34+Qpb+jp9dSUKBjIHSd71XdbGhzq2epHLeky8IhYqJ/ZuLBz4CwGXC27i/9RYKW1OPTda747k+/H209evhUk4nJheXCHqEAimUynIxkTkx8gYrPn6PK8eP0POdsXR46RUUJcgJJTw69l2Mp1FlR9TKvL/CKjtZs2hwc85GpfD+bydur7QqYZ6egrSs4YJGqWDuzkuWqVAQHoIlS5YgSRLr168H4OTJRTz9dH8GfHOZXlcu81/67f0np0+fzqvTx9Lt/WH89d9hyKfXf+fOnSiVSmbPnv2QnqD0RNAjFEipUpdpT8/xLRtZOOpVlo0djdFoZOC0WdQJbFtm7RHKhyy9gd0X4gpMluZf1Ymvnw1g48kovt5+wXRQkTu8ZZmumeputrzVuTbzd4fx35VEi9QpCA9SeHg4CxcuJDAwkOzsRC5cnMaYMe/SsmUzjm34lWle3vzvo8/R30ruGhwczN9//01gjRoA2Hfpkqe+5ORkxo4dS/fu3R/6s5SGCHqEAilUyjKb0xN16Tw7F88jJS6GBu078dL0WbhWEfuiCfDvxXgy9Qa6NPQssMxTjb0Z060ec3deYu3RGxab03On1zvUonl1F95eeZyb6dkWrFkQLMtoNPLaa68xd+5cVCojFy9NJyrqN/bsyeLjj5ajUGpobG2Nl5szu3fvBqBly5bUrFkTCVB5uGPXrl2eOkeNGsX48eNxdXUtgycqORH0CAVSKlXEXwvn4n/7CT95nMgLZ4m7Fk5STDQZyUnodVmF5k8qiay0NDbO/hzv2nUZvXwdwa+9gcbaxuL3ESqmfy/FU83Vhlrudvct93qHmjzbvApjfz/JCdtKKGTLzOnJpVIqmPN8AJl6A2N/P2mxegXB0qZP/4DGjR1wcNhCSupJlApratdaTk4OVKrkg0Jtmq9T1dOVa9eu5b1YksCY98+FNWvWoFAo6NWr18N6BIsRyw6EAjlXqkzYkUNcOX6k4EKShFprhdbGBo2VNUqNBq2NDTYOTtg4OqJUa1CqVChVKlQaLWorKzRW1qi1WtR5/rVCY2WFSqtl8w9fo8tI47kJM8ybiApCrmuJGfh63D/gAdOKral9GnM9MZMJui7USQi3aNAD4O1ozbQ+jRm54hj/nIvliXoeFq1fEErr1KlTrFy5gK9muXPt+gm0Gg/q1BmPldXt4WFJZQp6ZGM+faGShHzHNIfo6GimTp16TxLhikIEPUKBer8/HoNeT3ZWJvqsLPS6LPO/2VlZ6LMyzcey0tPRZ2WSk52NLiOdjOQkbkbeICcnB2OOHoNejz5bhz5LV6TcP73fH4+Du/gAEe6VrsvBy7HwDYcBNCoF815qRs9PwzniWQ8XXarF29O9sReta7kyeeMZ2tR2y7OiTBDKUk5OOuvWTSYqKpOhQ6JRqZyIjU3k9dffZNKkSahUKqKjo7FRm9I7XI+Ow8fHJ28lkgSG28HQ0aNHiYqKIiAgAID4+Hg2bNhAXFwc06ZNe1iPVmIi6BEKJEkSKo0GlUYDDvdPQ14chhw9+izdreApkxydzhRY3Qqg7F3dqFSnvsXuJzxanGzUJBZjDo2jjZpFg5vx5PILJGrtC7+gmCRJYkLPhnT/Zi8L915m5BO1LX4PQSiu+PidhJ56i9ZtMunf/13q1p2EUmlNx44dGT16NH369OHQoUPMmzeP94f0JTQzk+i0JDp06HBXTRLc0dPTo0cPYmJizN8PGTKEgIAARo8e/XAerJRE0CM8dEqVGqWdGiu7wocoBOFuGdkGbDTFS1ng29iX1d1SOXPu6gNpU10ve15rW4M52y/yZANPcx4fQSgLev1Nzpz9ECen5vj6foydrW++5T777DMGDRpEwE9LIDqaOZ+8hlptmlIwdepU5s2bR1xMDGdkmF6lCsePH8fdPZ9tXioQSX4QM1ErqJSUFBwdHUlOTsbBwaGsmyMIQj46fvEPnep58mnPBmXdlDyy9AaenvsvWpXEujfaoFGJXFJC2Th77iNiYzcR2GobWm3hQUpW7BWutO+O3dRXqPrMB3nOJf70E7FzvqHesaMPqrkWUdTPbzH4LAhChRF6I5nwhAyaV3cu66bcw0qt5LNnGnI6MonGc18nJTul8IsEwcJSUk4SGbmKmjXfK1LAAyCpTXPk5PyS0SpV+SYmrKhE0CMIQoUxd+dFKjtZF5iYMNfdmWenT59O3bp1USgU5mN3yy+77EcffUS9evXw9/enefPmbNmy5b73bebjhkITj1HvSptf29Dk5yacjBPL2YWHw2DQce78J9ja+lKl8otFvk5xa/UW+QQ9kkqJLIIeQRCEh+tKfDpbz8QwOtgXpaLgped3Zp7NlZtdtn379vleU1B22Xbt2nH8+HFOnDjBokWLePbZZ0lPT79vOzUqNZVsq1DZrjI5cg5j9ozhRuqNYjypIBSfLMucvzCB9PSLNGjwJZJU9OFVRW5Pjz6/BQKmPD35LmevgETQIwhChXDocgIAPf0rFVjmzsyzWu3tXdZzs8sWpKDssk899RTW1tYANG7cGFmWiYuLu287FZKMjcqOTf028Xe/vzHIBp7/63mOxx4v9BkFoaQiI1cSFfUb9epOwcG+UbGulW4tWZfvSici6/UkrV2Luuqjkw1fBD2CIFQITjamVSVnogqeKzNr1izatGlDs2bNilxvUbPLLlmyhJo1a1KtWrX7lpMkGYMRFJKCKvZVWNNrDdXsq/HurndJykoqcrsEoagSE/dx/sJEqlQehLd3/2Jfr1CqkCUZ413DWHFz5pB19iyVZ32FpHg0woVH4ykEQXjkBdf3xK+KI8N+OkJsatY950+dOsXatWsZP358kevMzS47Z86c+5bbsWMHkyZNYtWqVUjS/bM6KxTy7d3dAQeNAxNbTyRZl8yXR74sctsEoSiydNGcPvM+dnb18PX9qOQVKSVk/e05PWn/7iPhx0W4v/0W1o0bW6Cl5YMIegRBqBBUSgVLh7YkU29g1X/X7zm/d+9ewsPD8fX1pXr16hw8eJDhw4fzww8/FFjnndllq1evzpo1a5g8eTIff/yxuczu3bsZOnQof/75J3Xr1i20nQoJDHclAvF19sXP3Y8/wv7gesq9bReEkjAaswk9OQJJUuLvtxCFQlPiumQl5lVaOXFxRI4Zg23btri++qqFWls+iKBHEIQKw8VWQ+PKjoTFpd1zbsSIEURFRREeHk54eDiBgYEsWLCAESNGFFhfbnbZ3GueeeYZPv30U3M6/T179jBo0CD++OMP/P39i9RGxa3hrbuNbjoagG+Of4NRfjQmhQpl6+KlmaSmncOv8fdotaXctkdpWrIuGwxEfPghKCQqfTbzkRnWyvVoPY0gCI88W62KNJ2h8IJ3mDp1KlWqVOHAgQO89tprVKlSpdAJyQCvvvoqOp2OoUOHEhAQQEBAAKGhofe9RiFB7E01Sw/vy3M8wCOATwI/YXP4ZiYfmIzICyuURmzsZm7c+Alf33E4OPiVvkKlhJyTQ8LChWQcPETlzz9HddfE/keByMh8B5GRWRDKv9d/OUqm3sBPr7Qs66bk69mflvHfOXucHBMJGTv0nvNLTi1h1tFZ+Lv7M8J/BK0rtS50npAg3Ckycg3nzo/H3f1JGjX8xiI/P6daNUBV2RP5XCxOr72E/hlPkCQkFEiSCklSIElKZGSsrarg4tLGAk9iOUX9/BZ7bwmCUKHkGI2U5xhh9eCXeGLuEhJS82/kkIZDqO1Um3kn5vH69tfxc/Pjhfov0LVaV9RK9UNurVDRJCUd4dz58Xh59aFe3UkWC5gVqTLymWhs27Ujs5ctVy5NQ6m0QZYNt163V3YpFFqe6HjGIvd92ETQIwhChXImMoWeAQXn6ikPFJJEQV3okiTRrko72lZuy4HIAywMXci4vePYGLaRuZ3misBHKFBm5g1Oho7A0bEJ9epOQaGw3M+KdGuaWaXPP+Nq4mKstJVo02ZvnjKyLBMZuYpz5z9GluUK2UMpgh5BECqM64kZRCZn0aRq+dt7604KBRQ2cUCSJFpXbk3ryq05EHmA1GV9yTr2N+q6PaDzBLD3fDiNFSqEnJw0/j32ClrJmsaNvrNowAOQ/qYrDnWCUDk7IycYkKR7wwNJkszHZTn/MuVdxWuxIAiPrX/Ox6JWSrSpXb4nWCokqdCg505BlYLIychEIeng4lYI/xee/QkqNXlwjRQqjByDjlf2zGGr9DlvO4bQXuNi+Xs0tyfeeIys0DdISzuLpMg/PMg9npp6ihxDGvrsBLL1iSiVNlSu9JzF22VpIugRBKHC2HkulhbVXbC3Kt9DQEXp6blbtqTgVONetOw0BVY8Bws6Qp8fIKDoG0cKj6ZvryWwVeoBQDJOD+QeXp69Sby5D6MhE1ub2ji7tM63nEppC8CRo7czP0uSClnOwdOjByqV3QNpn6WIoEcQhAohJUvPvkvxjHuqflk3pVAKScJYzKDHIAGyAZx84H97Yf0I2PIR1OsBVo4PpJ1C+bc8MoGZ4bG86ePBymtnH1iqg+rVX6d69dcLLefq2pEmAT+jVNmhUbui0biQkLiX0NA3kOV7d2kvb0SeHkEQKoTNodHkGGW6N/Yu66YUSlnM4S0AGQkpN2mhUgVPTgZ9Fvz7teUbKFQI0To9Ey9F8KyXMx/V9EYpGckp4yQzCoUaF5c2ODr4Y21dBaXSBoVk6nk1ysXLn1UWRNAjCEKF8PvxG7Su5YqXo1VZN6VQCgUgF21lixx/EWNGAkaJvGNiDt4Q9AYcWgDpCQ+knUL59tGFG1gpFUyuXRlJklAgYyhwXWDZkSQlALJR9PQIgiCUWkRSJgcvJ9K3SZWybkqRKCUFMvkHPUuWLEGSJNavXw+GHGY815j6Ph64fHqTXYfC8xYOfAMkiZ3fvY1SqWT27NnmUx9//DGNGzc2Z4peuXLlA3se4eFbcD2WTfHJTPOtgpPaNBNFgVzsYdOH4faKrpxCSpY9EfQIglDubTwRiValoGvDirGMu6CJzOHh4SxcuJDAwEDTAaWK4Joq/h5oQ7Na1lh7N8t7ga0byQ2HMPab1XTvEpzn1AcffEBoaCghISH89ddfDB8+nPj4+Af0RMLDYpRlZlyO4tNLkbxR1YNeHk7mc0rkMh/eyo9Caep9PXAwmH92NSI+fmcZt6hgIugRBKFck2WZdccj6Fzfo9yv2splmtOTt6fHaDTy2muvMXfuXLRarfm4e20nEv2bYVulJVVqBd9dFaNWXmR8JwdcMy/niaScnJzMX6elpSHLMkaj2Mi0Issxyrx59hrfXI3h01qV+KRW3vlrCql89vQ42PvToMFX1PH9FDCQkXGlrJtUIBH0CIJQrh25epNz0akMaF61rJtSZArFvUHPrFmzaNOmDc2a5e3NMUoSkjH/CaBr1qxBobGm1wfzIOkqXP8vz/lvvvmGunXr0rRpUxYsWICHRyl32hbK1IIbcfwec5N5Davxho/HPRmPleV0To9CocLbqw9VqgxEobBCLscTmkXQIwhCuZaUYZocWc/LvoxbUnRKhQLumNNz6tQp1q5dy/jx4+8pa5QUpqXqd4mOjmbq1KnMmTMHGj8DrrXh3EaIv2gu89Zbb3H+/Hn279/P9OnTSUgQE54rqhtZ2XwZHs2Qym709sg/43h5ndNzJ1POnvIb9Ig8PYIglGt7L8YBEJeqw9vRuoxbUzR3D2/t3buX8PBwfH19AVNAM3z4cKKiougsKfLt6Tl69ChRUVEEBAQAEB8fzwY5i7jBXZn256VbS8RM/P39qVy5Mrt27aJ///731CWUfx9duIGjSslHNQtOyaCQyuecnjtJkqJcBz2ip0cQhHKtUWVTYr7yOJ9HlmW2nIrianx6nuOmDUdvBz0jRowgKiqK8PBwwsPDCQwMZMGCBYwYMQKjpEDK50OiR48exMTEmK955pln+PS9N5jWIgFClnHmzO1drsPCwjh+/DgNGjR4cA8rPDDbE1LYmpDClNqVsVcpCyynRKaos7byrBIEYmNj6datG76+vjRq1Ig9e/aYy97vXK6dO3fes4IwP6KnRxAEoRS8b+XlUSslzkSmcOhKAk828KSKs02ZtmvvxTje+vU4N28Nvz3d2Itm2Sd55ZVXePL9D0DTnNjYWF5++WXCwsLQarV8//33tG/fHjDN1xkzZgwXLlzG0VZJpl7JqVOnGDVqFO3atePo0aNYW1tjZ2d3+4PGpSYx1q2w3TqJD/+tz5UrV1Cr1ahUKr799lvq1y//2aqFvK5m6vjowg3aOtnRw/3+mbeVyBiK0NNzzypBYOzYsQQGBrJ582YOHz5M3759zT8/9zsHkJyczNixY+nevXuh987NJpQfWTYiy0YUBezr9TCIoEcQhHJNcWsyZ9vP/jEfS8vK4c3OvmXSngNh8cz8+xwnbiSjlCSeb1GV49eSWP/vCTbt+ZbAwEBTm2VFgR8mu3bt4r///sPNzY3nujTixU4evLMg3HyPDRs28Msvv6BSqdi4cSMDBgwgPDyc/StG4HnhEJtd27B2wwa0CtFZX5GFpmbw5JELAHxT3+eeict3U0hgKCD/U647Vwm+99575uOrV6/m0qVLALRo0YJKlSqxe/dugoOD73sOYNSoUYwfP57ff/+90GfK7ekxGnWkpp4mNe0caalnTP+mncPWthYtW/xRaD0PSqn+j5k5cyaSJDF69GjzsQULFtCxY0ccHByQJImkpKRS1wmQlZXFyJEjcXV1xc7Ojv79+xMTE5OnzLVr1+jRowc2NjZ4eHjwwQcfkJNT/pMlCYJQMF8PO9rWduO1tjVY8VorPOy16B/ibM6UTD3f77rE03P3Uufjv3lh4SFO3EimZXUX/vu4MzP7+/HXm61J2jIXOWgoWq321vCWgtWrV/P666b9jO78MAFo2bIlNWvWBEC6K6lPr169UKlMf5MGBgYSERHB6bN7aX1hBVur9WNUvY/57lrsQ3sPBMtLzTHQP+SS+fucIuxboihCT09+qwQTEhLQ6/V4eXmZj1WvXp1r167d9xzcWkGoUNCrV68iPZfBmMHVq/PYs7clR44O4MKFSSQnH8PGuhqODv7odDGFV/IAlbin5/Dhw8yfPx8/P788xzMyMujWrRvdunVj3LhxFqkT4J133uGvv/7it99+w9HRkVGjRtGvXz/27dsHgMFgoEePHnh5ebF//36ioqJ4+eWXUavVTJ8+vaSPKQhCGfNwsGLZa63M36uVCoylDHqyc4wcuJzA1tPRHAm/SapOT2RSFpUcrajpboungxVRyVmcjUoxD18pJKjmaktwfQ/+174Wbva3c+3Mnj2btm3bcM6tJlFHs6gjSRgy0u77YWIm3bHnVj7mzJlD9+7dydk1g1SlLR0GfsfQ60l8FR6Nl1bNi96upXovhIcvLcfAsFPhpOTc/u9elKBHKXHf1Vu5qwTzm5NTErkrCHft2lXka3Ln81SpMggP967Y2dVBoTD9v3Il/DvS0i9YpG0lVaKgJy0tjYEDB7Jw4UKmTp2a51xuD01x3qTC6kxOTmbRokWsWLGCTp06AaZJWvXr1+fgwYMEBgaydetWzpw5w/bt2/H09CQgIIApU6YwZswYJk6ciEajKcmjCoJQzigUYCjhTtNXE9KZs/0i287GkJqVQ1UXa9r5unMxJpXIpCyiU7KISs4yZ0JxsVXzRF13nm1Rla4NPFHkM5yU+0Hzz67d+E/ZQURSJvUkCQoZhsglAxQQ9CxbtozVq1ezeNEM/LcP5eCT3xKoseG96lacTsvk/XPX8dSo6ezqUKL3Q3j4ZFnmf6evsutmKgCz6lbl3fPXi7Qqy5Snp+Cfq4JWCU6aNAmVSkV0dLQ5CA8PD8fHxwdXV9cCz+W7gnDDBuLi4pg2bVq+bWjR/HcUCmu0Wvd7zkkooYzzDJUo6Bk5ciQ9evQgODj4ngClpO5X59GjR9Hr9ebxRYB69erh4+PDgQMHCAwM5MCBAzRu3BhPz9tp6rt27cqIESM4ffo0TZo0ueeeOp0OnU5n/j4lJcUizyIIwoOjlKRi9/QYjDJL94fzxZZzuNpqeaVNDbo29KK+tz2SJHEkPJFn5h1gy+j2+Hrak56Vg5VagVJZ+AyA3A+aenXrkJShJz0xji3fhWMb9GKBHyZ5SEqcM1OJjTqOh/ft31OrVq1i0qRJ7Nixg7h/pxFp5UmLoBcBsFIq+KlxTV49dYVBJy8zrU4VBldyNc9/EsqvlVdPsSchAyQtL3q78KSbA5wvWiCvkGT0xoL/G48YMYIRI0aYv+/YsSOjR4+mT58+HDp0iHnz5jFx4kQOHz5MREQEHTp0AGDAgAH5nlOr1XmmkQwZMoSAgIB7pp/cydrap8BzkiSV+cquYs/pWblyJceOHWPGjBkWa0RhdUZHR6PRaPKkXQfw9PQkOjraXObOgCf3fO65/MyYMQNHR0fzq2rVipPxVRAeV0qFhKEYQU9YXBrPzj/AlI1neL6FD1vfac87T9ahQSUH88RRpcL0b+4Hj62VqkgBD+Rdjh55/SpWlerh8OQo7Js8Td/+fZk3bx7APR80uTSuvhgkCcPirsRGHDHX+fzzz/PBBx/g4+PD8hWbCZwdjVqlNi9BViskFjeqgeu30xnevjVOdRvQpHlzduzYYa778OHDtGnTBn9/fwICAti5s/zuifQ4yMyMwONyH/qwhspaNRNrV0Z162ewSMNbUMC6qMJ99tln7N+/H19fX4YMGcKyZcvMq7Pud86iJCXyfYZyH4Zi9fRcv36dt99+m23btmFlZWWRBjyIOotq3LhxvPvuu+bvU1JSROAjCOWcUiEV6a9ig1Hmx72XmbXtAt6OVqz+XxAta7gUWCdATlHWA9+HjVaFs62anFuJCT+dMpFRw97A19cXjUaT58Nk6tSpzJs3j7i4OM6ft+ajjQlsT+/G1Z4LmTdvHhqNhhkzZvD999+Tdj2Cjz4exsoNZ/PcT6WQ2LtoAedQMST0CvbXLtN/wAAS4+ORJIm+ffuydNJwgju244KhMsHBwZw/fx5r64qR5PFRc/GS6Q97a7KYUacKDiolqTmmMKboc3qK3pt35zQTT09Ptm7dmm+5+52709KlS4t87/xIkhKKnGnowShWT8/Ro0eJjY2ladOmqFQqVCoVu3fv5ptvvkGlUmEwFD8GLUqdXl5eZGdn37MSLCYmxtxt7OXldc9qrtzv75xIeCetVouDg0OelyAI5ZtCKryn58bNDJ6Zt5+Zm8/xclA1/n67fYEBD9zR02OBVWEhh/ZhUycIAGdXF7Zu3crFixc5ffo0TzzxhLnc+PHjuXHjBjqdjoTEJELOHcHBRsF7Lz3H5o2LCAoK4uuvv2b7ljVcetOWpu275Hs/JycnAp3smFC7Eqdi40jLMRCt05OQkEBcXBzB17+CX/pQp2Y1nJyc+Pvvv0v9jELxGI3ZnDs/gbg403t/RtGKLm6mnDxKKbeXsfB6TD09FXcIU6KCDW917tyZ0NBQQkJCzK/mzZszcOBAQkJCUCoLziRZmjqbNWuGWq3O0217/vx5rl27RlCQ6ZdLUFAQoaGhxMbeXsa5bds2HBwcRJZSQXiE3G94y2iU+fW/a3Sfs5fYFB1rXg/i4x4NsNbc/3eT6tYE5ZJOkL6Th4MVTX1TUTvvQ2coesoMT+8mLNK/RLNqapoe/5BsXTIAkddCAKhUNaDAa8eOHcv4ti3RT/6AKlO/ZtiZq1wy6NE62bPstOmZDn/3P86fO0v4xXMlfjahZC5fnk1ExDIAruHDdy37ms+pbsUwRevpKSjtXwVR0Ya37O3tadSoUZ5jtra2uLq6mo9HR0cTHR1tTnQUGhqKvb09Pj4+uLiY/tLq3Lkzffv2ZdSoUUWq09HRkVdffZV3330XFxcXHBwcePPNNwkKCjJnnOzSpQsNGjRg0KBBfP7550RHRzN+/HhGjhyJVqtFEIRHg1IhYcznA+JiTCofrQvlcPhNnmlWhU96NMDRpmjzEnKn71iipwegdR0tF1V/ojeOKvI1p06dYsvWA6z9bS9Zv3RHij5FYkwongoDySo7KrtXK/DamTNnMnPmTLZv386oDz7kSL3G9EnJQD3tO+YumMxXR2NpaLeCtlVkVPFnCqxHsCxZljl3fjyRkSsBcHFpR9sGc9BqbM1lijOnRyGBQa64CSnLw/CWxTMyz5s3j0mTJpm/z025vmTJEoYMGQKY9omJj48vVr1ff/01CoWC/v37o9Pp6Nq1K99//735vFKpZOPGjYwYMYKgoCBsbW0ZPHgwkydPLv1DCYJQbtw9vJWlN/DdP5eYtzuMqs42/DoskKBaxctdo8zt6bFQ0KO+lWY/uxg9PbmrwFq37YfRqCQmKocL701gRJfKqNv40LwI2ZeDg4MhM4PPNFmMldWoa9Xln92HsFGr4cIW6nfoS0MvMZ/nYYmJ2WAOeFxd2uPntwCFIm8grpAkJIrWy6gCjGJ4q1RKHfTcnY9n4sSJTJw48b7XhIeHF6tOACsrK7777ju+++67Aq+rVq0amzZtum/dgiBUbKbhLdPX+y/F8/H6U9y4mcGIjrV5o2MtrNTFH2Y3z6uwUNCjUpjaoC/GPMe7lxu3adOKF2tfYmSNFDZq3fK9Rq/Xc/XqVWrXrg3Af//9R2xsLI09bVFGGRiRdBwb6wAAFu6LwdbBmU6pa+DSc1A7ON86hdLT65OJiFhB2OUvAXCw96NRo2/vCXhyqSSpSHl6irINRXlm6ukx9YAVtuXGgyL23hIEoUJRShJrj90gMimTA5cTaFndhYUvN6O2h33J61RaNuhRK4vf03NPHWprtMGTWJW8jqn7VQydXpmUhHjzpqTHjx8392gnJyejUqmwtbVlzZo1rLlxCWfZA93xK9QZWwdZlqlfvz7rdhxG+vd9WPc6vL4P7D0Lb4hQLDpdDIeP9Eeni8LKqgpZWTdo3Pg7VCrbAq9RSUXr6Snu6q1yRzL1VsqyAUkqm/BDBD2CIFQozao7E5WSia1WxefP+PFM0yooFKX7IFBZcPWWqT7TX7TZJVjRmmvXrl38G/Evr+9YjO97c0gebuCXhtXpdtdO3Llb8eTKzExj+L8nGaa6wZgp05gy5a7MuW4/wA9tYP3rMHCtKcW1YBEZGeGEhAzFaMyiVctNZGSGExr6Bkrl/YcUlZJEThF+9pSSVLF7esxrpwyUVfghftoFQahQxnSrx94PO/Hj4OY827xqqQMeuL2Te1E+eIoid06PvhQ9PQD7I/fjaeXMyoD6dHFz4NXTV9gQm3Tfa/ac2UuayoZ+9e7NQg+AnQf0+QHCdsLOKaVqn3BbcvJx/jvcGySJZk1XYWdXF4VkGs4yGu//c2Aa3ipackJjBf7YvnN4q6xU3HdPEATBQize06PMndNT8qDHYDSwJXwLnXw6oVUqmdegOr09nBl15ir7bu3blJ+/YhLwzY7Gt5JvwZX7BkO79+Hfr+HawRK3UTDJzk7kzNkPsbWtTcsWG7C1rWU6kTucU8hCc6UkFS1Pj1SxJzLfObxVVkTQIwjCY09h4aBHkxv0FPIX/v3suLaD2IxY+tTuA5gCs9n1qhLoZMv/Tl8lITv/urepfOiuySj8Bh0+BCcfWPsa5GSXuJ2PM4Mhg7Cwr9i3vx06XSx160xApbIznzdtsEmBG8rmUklFzdMjVewl6+aQo+yWrVfcd08QBMFCVHftvVX6+kzDGrqckv1FK8syP4b+SCvvVjR0a2g+rlEo+LZ+NQyyzEcXb9x7XY6em2oHfKyLkJtMpYUBSyE1CtYMBWPZ5k+pSGRZ5vKVuez9N4hr13/E27sfrVpuxMHBL085qYg9G0Ud3lJV8J6e28NbIugRBEEoM7e3obDML+Pcnp6cEvb0/BvxL2cTzzK88fB7znlo1UyvU4U/YpP48675PQajHgBFUScnV24KT38N5zbC8V9K1NbH0eXLX3Hlymy8vfsT2Gob9epOwdr63n0bb3/IF2V4q6gTmSvux3ZRg8AHqeK+e4IgCBZiqQ1Hc2lKuWR94+WN1HWuSwuvFvme7+PhRHc3R8ZcuE5ctt58PPcDUVmcHqumL0PAQPj7Q4gVW1Tcj8GQxdmz4wi/+gO1a4+jbp1PsbauUvAFRezZKGqeHtOcnor8sZ07x0n09AiCIJSZ3OSE+W1vURJqc09Pyf6ivZR0CT93vwITuEmSxGd1TR+2Y87fMK+GMdz6kFUW9y/pHl+BnSf89S5kF2E+0GPqUtjnRMf8ga/veKr5vFZoeanIE5mLmqdHeiSGtwqb4/QgiaBHEITHnkIhIUkWXLKuNM3pKenqLQkJpXT/zNLuGjWf16nKpvhkfou5CYB86wNRWdxtKdXW0Od7iDwOK54FfVaJ2v0oS0k5yY0bP1O92hv4VB1apGuKPpG5iEvWxfBWqYnkhIIgCJgmMxsttXpLUbqeHjdrN/65/g/P1HmGui51Cyz3tIcTz8Q78/GFG7R2ssNNfWtYrSTPUb0tDFwDS7vDpvfAtws06F2i9j8q9PoUwIjBkMn5CxPRar2oVu31Il9fnInM+iL39JT/oCc19SzxCTsxGjIxGrMxGHVkZUWQlmra7FYqJKB/kETQIwiCgClBoaV6enLn9OhLGPRMbD2RUTtG8fxfzzOowSD61u5LDcca+Zad5luZ/UlpvH32Gr8F1EJlzCHNWMIhkOptoMNY2D0Tji+DCUlQRnsklTWjMZv/Dj9NVlYEkqRBo3GhcaPvUCiK8bFZxKCnqBOZVUgYKLuAoSji4neYs1CrVI4oFBoUCi1arQdeXr1xcAxAq/Uos/aJoEcQBAFTT4/l9t4q3eotL1svlvdYzoKTC1h+djlLTy1lfZ/11HSseU9ZR7WKOfV8ePZEGBMuRWBlzCa9NPszdRwLUSFwYTPsmwNtR5e8rgrs6tUFZGVFAFCp0gBq1XwXtdqpWHWYh7e4/8+VKU9P4fWV956exMR9nDo1Cje3YBo1nF3gBqtlqfy+e4IgCA+R0qJBT+42FCWfu6BVanmzyZv81O0nZGRSswvOwtzOxZ6pvpVZdCOeNJUNGfpSJBuUJHhxFbT/ALZPgKM/lbyuCionJ5Wr1xbiU/VVOncKo17dycUOeKB4w1tF6umRJIzlNDlhcvJxToa+jpNTKxo1nFUuAx4QPT2CIAiAKeix1PCWVmX6hb/pymbCbl7FRm2DrcYKW7UNdhprfN1d8HZwxEpphZXK9LJR2aBValEq8g5fKKSifci9WsWdICc7tm2Zw1OKBGBA6R7iiY8h8yb8+RYYc6DFq6Wrr4LQZcdz4fxEQMbHp3TPXJw8PUWdyCxLCgxGwz0/J2UpNe0cISdewc6uAX6Nf0ChKEJyzDIigh5BEARAqVBYbCKzm40dVoZaxBpOExN/HKRsJOmOuu+TDkej0GClssJaZY216vbu3EXZpLGBnTUNso6CJf7KliTo/qVpXsqmD8DaGRr1K3295djly3MIvzoPSVJSv950tFrPUtVX1AzERV+9ZQqAc8pJ0GMwZBIb+zcXL83E2qoqAf4/FrqjfFkTQY8gCAKgVFhuybqVWsPhV9abvzcajaRnZ3MzM42IlBT+t+wALWvaMapzNTJzMsnKySLLkEVWThYZORl5vs/MyaR1pdbUca5TtJurtKBLs8hzIEnQdQZkJiGvfZXY+Hg8O96bJfpRkJ0dz5Xwb/Dy7EOdOp+iVjtaoNbi5OkpvDZzEk3ZQFn1pciyTHLyUaKi1hITuwmDIQ1X1w40qP8FKpV9GbWq6ETQIwiCAKgUCoslJ7ybQqHA3soKeysrfJzd+KSrmg/WnGRQs8p0qONu2Zt5NIQD34I+05R/p5iiLp3gxqk9eNRrg6ePL1Y29tB3PhEpejz+Gcvgi/X4tE9TarnbFV5ZBaHXJ3Pk6LNoNG7UqPGmhQKeoifjK25Pj6EUG9mWlF6fzI2IZURFrSUz8ypWVpXxqfoK3t59sbb2eejtKanyOSNKEAThIbPknJ7CPNOsCm1qu/LR76Gk60r3AdalSxf8/PwICAigXbt2HNf5QHYam1cvpnnz5vj5+REYGMiJEyfM1xw+fJg2bdrg7+9PQEAAO3fuBEA2Grn582BmffIunVs3x6+qE/Oeq8qJ2f1JjbqERjJwLCyGzl/tJjvn0dig1GDQcTJ0BHp9Es2arsLGprrF6i7qRGa1oqgbjt7u6XmYDAYdx44PJDz8exwdm9KkyTJaB+2iZs23K1TAA6KnRxAEAbDs6q3CSJLE9L6N6Tp7D7O2XeCTpxuUuK7Vq1fj5OQEwLp16xjy4afsegoGjhzDnn2HaNiwIXv37mXgwIGcOnUKWZbp27cvS5cuJTg4mAsXLhAcHMz58+e5dvIf1uw9R6bamUtv6VibUIvXlp7lz1o3aKu5AMCLzb2ZfySZN389xpznm2ClLvu5JSVlNOoJOTGElJQTNAn42aIBDzyIDUdNQVRCZrKpfhRISKaM4kiAAklSoLj1UkoKFJKpd+PuLU1kWcYoy8jIGOX/t3ff4VFWaePHv88zJb0nJLTQgkDA0BQIlqUL4oroq7uKgpUVe2FFLKsgAq/u6mJZii/ys7GIKLZFigqIqIhAIKCwtEAgCQmBFFImU87vj0kGIoHMJJlkwtyf68qVPP08x9G5vU+zY3fYcCgbdnsFDuUMxMMDW6DrOkePvk9JyV4uvfQzwkK7elgLvkWCHiGEoHGDHoB2MSE8NvwiZn+1mz/2bEWvtpF1uk9VwANQWFiIphvYb+pKTNBvdO/eHYArrriCw4cPs3XrVhITE8nLy2PYsGEAXHTRRURGRvLVV1/RKbiAD3dZeWjC5cA3xHYbyPBRSezuOIIOgy7l8I+f8OT1A7k0OY/7F29l/Ns/s+C2vkQGm+tZG43Pbrfw3/8+T2HhVnr3fp/IyEu88BT3Fth0d8HR4MpRgVeknQROelwaDQcoUG6OCEw1b2ZJ/6EcOryAhISxzT7gAQl6hBACqPy/7UYMegDuvKwDn2/P4smPd/DFg5djMtStx8H48eNZu3YtACtWrCAxawX5r0zlh/XfMvAPQ/j8888pLi4mIyODPn360LJlS5YuXcpNN93E5s2b2bNnDxkZGSSlxHG40EFCdChYwK4Zad++PYcPH6Z10t20TuoJwLDkeBbf05/7Fv3CjGkbefTePrTu0DD9YBpDQcEv/Lb7KcrKDtO1ywyiImtezb6+qpq3qDXT496Co6PaXMobjk2U2a0oBQoFqMq/ndvODI6jMoNT+YMDBxoKHVU5ZaKmgY4GmoYBDU0zoOuVWSKMZJXl8/rx1kzetJCx1pO0bzep3vXhCyToEUIIGj/TA2A06My+PoUxb25kwXcHuH9wUp3u8+677wLwzjvvMGXKFFb834ssu3E6U6dM5pRVIzU1leTkZIxG53/yP/vsM6ZMmcKsWbPo3r07l19+OUajEc3gzNhE9hnLT3md6Hz5OFb+a0GNz+zbLpppg7uw/9/7+fR/t3BRv3gG3doVk9l3m7uUcrD/wCscOjSX8PDe9Lv0c0JD3RwVVwcNPWQ9wGjmfzpe0SBlc0fZ1k94q/AK/hB9iuDgdo32XG+SoEcIIQCjofE6Mp+pR+sI7rmiI3O+2cuoHgl0rMeoqAkTJnDvvfeSb4hncEczgx96GPpOwGKxkJCQQHKys+9Qz549Wblypeu6bt260b17dwyGAhIjdApLbVx763MAZGRkMGLEiBqfFxHibG5p2TeOfb/kEhRm5vIbO9e5/N5UUXGc9J0PUlDwM506PUG7xLu9vvClu3163A16GpNDKTKMPQnXC+jb4famLk6DkdFbQgiBc8FRu6NpRiQ9MqwzLSMCmfpJukcTJBYUFJCVleXa/vTTT4mJiSE6MoLsIhsYAwF44YUXGDJkCElJzkxSdna265q33nqLkJAQhgwZgmYwcWOykffe/wCAgwcPsm7dOq677roan28yOr/UY3pE0f2KVqSvPcKPn+7HbvWdkV02WykHDizgp00jKSnZR4/ur9G+3V8aaaVvTxYcbYTieOClgzl8nV/Mv3p0pkN4y6YuToORTI8QQlC14GjTPDvQZGDW2Iu55f828eEvmdzcz71hwIWFhdx4442UlZWh6zpxcXF8+eWXaCXH+NtaCxs+/Ss27WlSU1NZuHCh67oFCxbwwQcfoJSiW7duLF++HE3T0I1G/jowgD+lldGpUycMBgNvvPEGsbGxNT7fZKqcIdjmYNBNnQkMM7NlZQalBRaGjO+GpjftCu0FuaVsWD2Z4FarCA7qTJ8+7xEQ0MDzIp2HK9NTa0dmfCrT83HOCf556BjPdGzJsJjwpi5Og5KgRwghAF1vukwPwMCkWG66pA0zV/zGkK4tiA8PrPWadu3a8fPPP599IGMjb10bBPd/DXFn91l57rnneO65587arxvMhJg1/vnyiyR1r300k8noDHqsVgcGg06/azoQGhXA2vd2k5dZjN2maNstmm4DWxKX2Hiz9dqtDrauPsSWrw4REJ1MYqtVdGw7p1EDHvCkI7N7Q9Ybw9bCEh7bk8mNCVHcn9iiqYvT4CToEUIIKjM9Tfy989TV3fh2dx7PfbaLebf1rfuNiiubr8ISPLpMNzo7MiubexMmnpnpqZJ8WSuCwsxsW32I4PAA9m/NJX3dETpfGs9l/5NESIR3F1A4uuck6xbvoSivjF7D2xLV3sbRk+CwN/4sxqf79Jz/g+UrfXqyyiuYsPMgPcOC+XuXtmfN7XMhkD49QghB1eitpu2LEhlsZtq13Vm5K4eVO7Nrv+BcCo+AORQCPWua0CtHd9nsFW6db64cqWX73ezMHVJiuX5yX0ZO7MGEWQMZOqEbR3afYPFzP7FjbWaDLex6ppJCC9/8v1/59NVtBIaYuOnpS0kdm4QpwBnIORyNO4uxk3trb1XN07No0SI0TePTTz8FIDc3l5EjR9K5c2d69OjBd99957rmfMcGDRpEhw4d6NWrF7169eLVV1+ttaRKKR7fk4lR01jYoz0B+oUZHkimRwghqFyGoqlTPcDVFycwrFs8f/tsF6mdYokIcnPFdLsN1s6A1Afh0EZo4/lkewaD81nKZnXrfHNlpsd+ns5QukGna2pL2qfE8tOn+9mwdC+//ZDNoFu6Et+h/v1FivLL+HVDFjvWHkE3agwa14Xky1q5+hPplSvOO5pgvSpnpkR3oyMzlGYd4a233mLAgAGu/U8++SQDBgxg5cqVbN68mbFjx3Lw4EFMJtN5jwG8+uqr5+yAXpNlx06y9kQx713cgTizm5+5ZkiCHiGEwNm85a0FRz2haRovXNed4a98x+yvdjPr+ovdu/DUMfj+Vdi1HE7lwqAnPX62oXLGX4fd3aDHmemxujFaKzDExKBxXek6sCXrF+9h2Uu/EN0yBKvFTmhUAAkdIohrF0bri6IIDj/3DM92u4MTR0s4llHEgbQ8Mn89gSnAQPLlrbjk6vYEhlT/wtYrMxZN0bxlqTgOODCZIs97nq4Uh2b9jfXz3+Txxx937V+6dCn79u0D4NJLL6VVq1asX7+eYcOGnfeYp/IqrPxt71HGtohkeGzzmWSyLiToEUIInEPWK5pq+NbvtIwIYsqorjz76U7G9GrFgI4xtV8UUtlJ92SG83enIR4/t2pyQofNveYtkyvT436wmNAhghufvITffsgmIz2f0KgASgos7N1yjG1rDgMQHheEw+bAHGQkLjGMsJhALKU2jh8uJvdwMXarA03XiG0Tyh9uvojOl8YTEFxzdkI3OL/mmqJ5q7goHYDwsPMHrt8vnE/Qxb3o2/d0P678/HysVisJCaf7ZVXNjn2+Y1WefPJJnn32WZKTk5k1axYdO3as8dlKKZ7eexRNgxc6t6nTezYnEvQIIQTOyQnLrE2f6akyrl8in207ylOfpLPi4StqX9jTaAZjENjKIDEVWnT3+JkGU1Wmx72sSEBlmewerriuG3S6X9Ga7le0rra/pMBC1t4Ccg4Uoht1KspsHM8s5kBaHgaDTlTLYAaM6Uh8+3BiE8Pcmv1Z151fc8rhXvaqIRUX78RojCAw8NzBxM6dO9n+1X+I+kfNM1/XxXvvvUfbtm1RSvHmm29yzTXX8Ouvv9Z47tzMPD7PLWBecjtizRd+SHDhv6EQQrjBoOuNvgzF+ei6xuwbLubqOd/zxrf7mHxVl9ovimgNF42Eq16s0zONVaO33Gzeco3eaqAMWUhkAJ0vjafzpfENcj8AXXcGRk2R6bFYjhEU1Oa8o6A2bNjAiSOZlN7yR9oHmMjJyWHixIlMmzYNo9FITk6OK6OTkZFBYmIiMTEx5zwG0LZtW8DZVPrAAw8wefJk8vPziYmpnjHMKq9g1oFs7mvbguvio7xRBT7nwuyeLYQQHjJoNMkyFOeT1CKM+wcnMW/9fnZlFdZ+QXAMlByv8/NO9+lxL9NT1VnY00xPY2rKjswOZUPTzt8peNKkSfxj605aL1lBRkYGAwYMYMGCBUyaNIkbb7yRefPmAbB582aOHj3KH/7wB4BzHrPZbBw7dsx1/48//pj4+PizAh6ARUePE6hrPNq+4YJMXyeZHiGEwJnp8cZQ6vqaNKgTX+3M5uYFP/HLM8MxG8/x/6o2C+T+Bh0H1/lZemXzlruZHk3TsKNw+MCot3M53aenCUZvoeFc//z8jJqGtYZO9P/7v//LbbfdRufOnTGbzbz//vuu0VnnOlZSUsLo0aOxWCzouk5sbCyff/75Wfcuszt4Pyufm1vGEGb03UViG5oEPUIIARh038v0AJiNOo8Ov4i/vLeF37KL6Nk2suYT968FSxF0v67OzzJVNm/hZtADzq90u8336q1KUzZvaboZhxt9iYyVa28ppVi3bp1rf3x8PKtXr67xmnMdCwkJ4Zdffqn1mctzT1Jgs3NH65qXGLlQSfOWEELge316zpQYHVz7Sb9+CrFdoEW3Oj/H0+YtAId2/nl6mlrVhIuqhkzP7ycDnDlzJl26dEHXdde+KnfccQcpKSn06tWLSy+9lG+++cZ17Oeff2bAgAH07t2bbt268dJLLzmfrZtQqvagx1DZ5aexEmZKKd4+cpwh0eF0CPbuDNm+RjI9QghB1YKjvhn0VE2aaDjXAp42C+xeAQPurddzjK5Mj3tD1gGUhk83bxlcfXqqZ3oyMjLOmgxw2LBh/PnPf+bOO+886z6vvvoqkZGRAGzbto2hQ4dy/PhxdF1n4sSJTJ8+nWuvvZYTJ07QtWtXrrnmGsxmEw5H7XVpquzobFMKI95f+uHnwhJ2nirjqZQLZ/V0d0mmRwghqFqGouG/vEeMGOHKEFxxxRVs27YNgJUrV3LJJZeQkpLCgAED2L59u+ua32cO/vnKywDEhweyd+9ehg8fTs+ePenevTsffvghHFgPlkJIHlOvsuqGqqYgTzI9mkfz9DS2qj49Z2Z6HA4Hd999N6+//joBAaczHf369TvnfDZVAQ84V7c/k6ZpFBQUAFBSUoLZbCY6OhqbrRiDIajWMhorg57GWnR04dHjdAwKYFB04y0C6yvqFfTMnj0bTdN45JFHXPsWLFjAoEGDCA8Pr/ZBOJ+5c+eSkpJCeHg44eHhpKam8tVXX7mOZ2RkoGlajT8fffSR67yaji9ZsqQ+ryiE8BMG3TsrXS9dupQdO3aQlpbGY489xu23387JkycZN24c77zzDjt27ODll19m3LhxrmsmTpzIU089xbZt29i4cSMfLZpLxfHD/GnBj6SOvIHky65i+/btrFu3jieeeIKjP3wIMUnQIrl+hdU0KpQBzYPmLaWB8uXmrapATp1+p1deeYXLLrus2mSA7njyySfp1KkT119/PR9//LFrtudFixbx7LPPkpiYyEUXXcTMmTOJi4vkeP5aIsJ713pfwxmZHm/LtlTwn7wC7mwTi34BLihamzoHPZs3b2b+/PmkpKRU219aWsrIkSN56qmn3L5XmzZtmD17Nlu2bOGXX35hyJAhjBkzhl27dgHOOQeys7Or/UybNo3Q0FBGjRpV7V6LFi2qdp4na48IIfyXt5q3fp8h0DSN/fv3ExMTQ/fuzgkEr7jiCg4fPszWrVuBszMHocGBPHh1LwZ3aUHB0X18eTKBPTnFxMXF0bNnTz78bKVz1FYDfInZMXjUp0dp+OSotyp61Xpilc1bO3fu5OOPP+aZZ57x+F6zZ89m//79LF26lCeeeIKKigrX/lmzZnH48GF27drF008/zdffTKeiIp+2bW+v9b7Gyn9sjTE35rtH8wnUdf6UEO39h/mgOvXpOXXqFOPGjeOtt95ixowZ1Y5VZX3O7IFemz/+8Y/Vtl988UXmzp3LTz/9RPfu3TEYDNWm2wZYvnw5N910E6GhodX2R0ZGnnWuEELURtc1bF5aZX38+PGsXbsWgBUrVpCYmEh+fj4//PADAwcO5PPPP6e4uJiMjAz69OnDokWLGDNmDM888wx5eXnMnz+f8denArC63yUcO/A9D/67LXOubsUPGzfSPukUtBvYIGW1aQbwZPZin+/T48z0qMpMz4YNG8jIyKBz584ArskAs7OzmTRpklv3HDZsGA888ADp6em0a9eO5cuXu1oVOnbsyIABA9j4/XquG3sJISFJtZexkZq3LA4H72Xl86eEaL8apn6mOmV67r//fkaPHl2nhc1qY7fbWbJkCSUlJaSmptZ4zpYtW0hLS+Ouu+6qsWyxsbH069ePt99+G3WeD5HFYqGoqKjajxDCPxl1DS/FPLz77rtkZmYyY8YMpkyZQkREBMuWLWPq1Kn07duX1atXk5ycjLFypFFNmYOqZQTee/ddOqhsvpt9J2Num8jQ7nEYdSCqXYOU1Y4BPOjTozTNp4Me3Vg90zNp0iSys7PJyMg4azLAc7Fara7FPcHZ5yo3N5eOHTsSFRVFSEgI3377LQDHjx/nxx/XExeXQUK8e32sTI3UvPVeVj4nrDbuauNfw9TP5HGmZ8mSJWzdupXNmzc3aEHS09NJTU2lvLyc0NBQli9fTnJyze3TCxcupFu3bgwcWP3/bKZPn86QIUMIDg5m9erV3HfffZw6dYqHHnqoxvvMmjWLadOmNeh7CCGaJ13zXqanyoQJE7j33nvJz89n8ODBDB7snEjQYrGQkJBAcnIyx48frzlzsHEjycnJtG/fnlVffsYHmw7x9PKdHF09mRFtdCg76XyIUs75egLrtlq2DaNn8/RooHy5eUvXUUrDoWqfp2fGjBnMmzePvLw8du7cyQMPPMC2bdsICQlhwoQJFBYWYjQaCQkJYdmyZURFOZduWLp0KX/961+x2WxUVJQxZoyJQYPG0rLl/7hVRmMjBD2nbHZezTjGn1pG0yk40GvP8XUeBT2ZmZk8/PDDrFmzhsDAhq20Ll26kJaWRmFhIcuWLWPChAmsX7/+rMCnrKyMxYsX8+yzz551jzP39e7dm5KSEl5++eVzBj1Tp07lsccec20XFRW51iwRQvgXb/TpKSgooLS0lFatWgHw6aefEhMTQ3R0NNnZ2bRs6Rwy/MILLzBkyBCSkpKw2+2uzMGQIUM4fvw4mzZtcv236tixY8TFxXFLv0QWL/uCNfvzWXFzV3j/BghNgIpTUFECj/0G4Z4PSXaggycT+ekayoczPZqmgdJrnKcHqnfFeOaZZ87Z12fjxo3nfMawYcPYsmULDoeNrdvGYSnPomvXF8+75taZTjdvuXV6nczNzOWU3c7j7f27+4dHQc+WLVvIzc2lT58+rn12u53vvvuON954A4vFgsFQt3ZCs9lMUpKz7bNv375s3ryZOXPmMH/+/GrnLVu2jNLSUsaPH1/rPfv3788LL7yAxWKpNiyxSkBAQI37hRD+x2Bo+KCnsLCQG2+8kbKyMnRdJy4uji+//BJN0/jb3/7Ghg0bsNlspKamsnDhwspyGKplDqxWK4888oiruf+LL75g9uzZGAwGWiS0JPn2mUwOasGTnXfQIX8DWkkenNiPytiAlnKTx2W2aUbP+vTovp3pAVBKRynvjzDLOPQvCgu30rfPvzGZ3M+0ne7I7J16zKuwMi8zjztbx9Im0OyVZzQXHgU9Q4cOJT09vdq+O+64g65duzJlypQ6Bzw1cTgcWCyWs/YvXLiQa6+9lri4uFrvkZaWRlRUlAQ2QohaGTStwZehaNeuHT///HONx956661zXleVOajJ3Xffzd133+3a/vngCe5Y9DNDjvTmb9eM487St+GH19A+uQfLx/dRgRGrZqICMzbNhN0YRG5QJ5Ruqvwxogxm0E1oBhPdVAmaB81bvp7pAZyZHuXdtbcKCn7h4MHX6dD+ASIjL/HoWqPu3Y7Mr2Ycw6DBQ+38Z2HRc/Eo6AkLC6NHjx7V9oWEhBATE+Pan5OTQ05OjqvTV3p6OmFhYSQmJhId7RwiN3ToUMaOHcsDDzwAOJuZRo0aRWJiIsXFxSxevJh169axatWqas/at28f3333HStWrDirbF988QXHjh1jwIABBAYGsmbNGmbOnMnkyZM9eUUhhJ8y6JpPD70+l34dokl7bgQzV/zG9C9/5dTAUdyvXifPEM+hiyagbBaUzYJmqyCy6DdsljJCyo9hUFYMyoqubBiUDaOyYlB2rJgwJHR3vwA6zn5EPkwp3a0+PXVltRayc9cjRET0oX37+z2+3pt9ejLKLLyXlc8THRKIMskiDA1eA/PmzavWOfjKK68EnPPn3H777QDs37+f48ePu87Jzc1l/PjxZGdnExERQUpKCqtWrWL48OHV7v3222/Tpk0bRowYcdZzTSYTb775Jo8++ihKKZKSknjllVe45557GvoVhRAXIIPe8JmexmIy6Ewd1Y1lW47wyg8n+SRmOY+N6MK1PVvV6X4eje3RNfD1elMG8GLQk5e3Goslm759PkTXPf9a9WbQ878HsokxGbmrTe2tI/6g3kHP7+fjef7553n++efPe01GRka17aq27NrMnDmTmTNn1nhs5MiRjBw50q37CCHE7/ny2lvuMBt11k4ehMOhaBHeeKNzNF1D2Xx3RmYAlO7VVdaNxvDK324sDFsD14zMDfz5Sy8uZXluAX/v0pZgg6w6BbLgqBBCAJWrrPt4M01tYkOboP+irqH5eLColA54r09PQEALACyWXEymKI+vr+rIbGvganxxfzZJwQH82U9nX66JhH5CCAEYdGfXlObYr6cpaQYNfDzRgzJ4tU+P2VwV9Byr0/XeWHB0w4li1p0sZmrHlq6O0kKCHiGEAJyZHmj4JoYLna5raD5fZTrKq0GPsxdURUV+na5v6D49SilmHMiiT3gwV8fWbZLKC5U0bwkhBKeHDTuaeRNXY3Nmeny8zrzckdlgCMBgCMZqPVmn682Vn72KBvrsfZFXyPbiMj7pleT2BIn+QjI9QgiBM2MBkunxlGZoBpke5d1MD4DJGInVeqJO1wZUZhktDfDZszoUsw5kMTQ6nIFRobVf4Gck0yOEEJwxQZwEPR7RDRqar/fpweD1oMdoisRqq9ui1QGVnz1LA6z9tjg7n4yyChb26FDve12IJNMjhBA4FxwFCXo8pRt0dHy7zrw9egvAZAzHZi2s07Xmys9eeT2DnhK7nX9k5HBDfBTJoUH1uteFSoIeIYTgdKbH2yutX2gMzaB5S8Pg9bW3jKZwbHXM9GiaRqCu1bt5a+7hPAqsdp7o4N+Lip6PBD1CCIHzyxtAYh7P6AYN3ceDHtBReLl5yxhR5+YtcPbrKbfX/cP335JyXjt0jHvbxpEYJOtNnosEPUIIwZmz4krU4wndZMDg60GPMoCXgx6Tse6ZHnD266lrpseuFI/tPkzbQDOPtZcsz/lIR2YhhEA6MteV0aRh9Pkq8/7oLaMxHGsd+/SAM9NT147Mi44e55eiUj7tnUSgLDdxXlI7QgiBc8FRkKDHUwazAQMaNpt3g4r6MXi/eauyT4+q41w7de3Tk1lewcwD2dzeOpYBkTJEvTYS9AghBBL01JXBVDnHTLlvBz14uSOzyRiBUlYcjvI6XR+o6x6P3lJK8cSeTCKNBp7u2LJOz/U30rwlhBCcEfTIjMweMZoNAJSX2QgJNTdxaWqm4f0+PVUrrVtthRgM5x4unm2poNyuCDRoBOq6qy+ZQfM80/PRsZOsPVHMexd3IMxoqHvh/YgEPUIIwemgx2aXoMcTpgDnl21ZuXfnwamfRhi9ZXIGPTZrEQTU3Jm4wGqjzw+/nnNWoy4hgW4/70h5Bc/sPcIN8VEMl/W13CZBjxBCIM1bdRUYWBn0lFqbuCTnYwC8W76qTM/5RnBFGA10DAogxmzk4XbxlDsc2BUoFEpBv4gQt55lV4oHfztEmMHAzM6tG6T8/kKCHiGEAIyV6x9J85ZngoNMAJwq9d1Mj6YZcKi69bVxl8nozLacL+jRNI0bE6KYcyiXAREhhNSxSWpeZh4/FZSwrFcnIkzyNe4J6cgshBBA1UhfyfR4JiS4Kujx9UxPI/XpqWXY+g0J0ZQ5HPzneN2Gt28rKuWF/Vl0DArgsqiwOt3Dn0nQI4QQgKEy0yN9ejwTGuIMesrLfDfo0dAB747eMhgC0fUArLaC857XNtDMwMhQPsrxbEV2h1K8l3Wc67ftBWB/mYU5GcfqPETeX0nQI4QQnJ6R2SFfIh6pCnpKfbl5CwNo3h9SbzJFY62oPZi5MSGK70+e4mh5Ra3n5lVYuWX7flJ/+o2/7jlC2RmZyFkHsznkxj3EaRL0CCEEp9fesknzlkfCQ5zD1C2+PHpLM+LtTA+A2RxNhbX2oOeauEgCdY1Pjp2s9Vy7gm9PFHOovIIAXcOkaSxO6cj3/bsCuBU4idMk6BFCCM5chkLW3vJESJAJO4pyiy9PTqjj7T49UJnpcSPoCTMaGBUXydKcE7U2TyUEmPh7l7bEmoxYHIqr4yIYEhNO6wAzgbrG1qLShiq+X5CgRwghAF2rCnqauCDNjNmoY9OgwoczPZrWOM1bZlMMFW40bwGMaxnN3lILk/dk1ppdvLVVDEt7deJ/4qP4S9s4AIIMOknBgfxcWFLvcvsTCXqEEALJ9NSHTYOKCt/N9Gg0VvNWjFuZHoDLosJ4rVsiH+ac4I6dBymtJdpODg3ijeR29Ak/PZdPZnkFl4S7N7ePcJKgRwghON2nRzI9nnPoYPXh5i1Na7zmLXczPQA3JUTz7sUd2VhwihvT9pFf4X62rMLhoNBmJy5A5unxhAQ9QgjB6dFbZVY7DunM7BGHrmGr8N1oUdOMoHm/fAEB8dhsBVittXdQrjIkJpyPeyWx61QZt6UfcHsI+rbKvjxdPVi6QsiMzEIIAYDJoGPQNSZ/tJ3JH23HbNQJNOoEmgwEmgwEmQwEmnQCKrerjlXtDzQZCDAZCDDqzh+TgQCDToCpctvoPGau+vuM/eaqa4zOMhwrsmBXCl1z9jUKMOqEB5rQK5vgfI0yaNitvhv0gN4oQU9UdCoA+fkbSEi41u3ruoQEEmE0UGyzowB3/innVWaFPsjKp1dYsKtPmjg/CXqEEAJnh9ylf0nlyMlSyq12yq0Oyq12ys7422KzU1ZRuW2zU1RupczqwGK1u66psDu3LTZHnYa/axrU9D/7ugaRwWYig01EBZsxG3SMBg2jrmHQdefvym3j77YNuobJoBOYUYoxqwzVLgSjQUOrPEev/G0w6NX+Nhp0dIOGsXLbUPXMymuMlftsdge61Yebt3Qj2L1fvsCABMJCu3M8/1uPgp5n9h6hyGZnSc9Obgcvw2LCCdI1Psg+wf2J8XQMDqhrsf2KBD1CCFGpb7so+raLarD72exVQdDp3xabMyA6/fv3+x20CAsgyGRA4ZwssbzCzslSKydLKzhZUsHJUitWuwO7Q2FzOH+XWR3YLae3rXZVeVxhdzgDsBv2VmY7jjvndlFAQ4y5ikWjyK38RNPQMKCbC1n12R9de848evr3799Bq3bcdVTTiW4VgtFkOL1Xc55rtZ4kP389Stmdo8Zq8WH2CRZnn+DVrm3pGhKIzaGwKYVdOX/bFGf8rbArXH+PjI1geW4BVplQ020S9AghhJcYK7MlweamLonTps8PsPvHbMa/OBCHUiiHwmF3rvDt/NuB3a6w2h3YbA6sNoXN7sBqs2O3KWx257bd4cBWuW13KGw2O927xp71vEWLFnHnnXeyfPlyrrvuOmbOnMk777zD3r17+eSTT7juuutc557vmFKKadOmsXjxYgICAoiNjWXt2rVuv3f7zqPZs+sIhjOGrSuU66/Tv88MHmo67qyn0kILYVFgMhuqn68chIZ2JSAgHne6zGaWV/Dw7sMAPL47k0d3Z7r9TlVMmkasLDrqNqkpIYTwE5quoRzK2ayF5lyH0+SdZ2VkZPDWW28xYMAA175hw4bx5z//mTvvvPOs88937LXXXmPHjh3s3LkTs9lMTk6OR2Vp2+kS2na6xPOXqEF5iZWFj2/g4ot70CmlRb3uFW82MqNza5QCs65h1DQMGhi1qr81jBqVv08fP3M7OTQQsy5jktwlQY8QQvgJXYfGGJjmcDi4++67ef3113n88cdd+/v163fOa8537OWXX+bbb7/FbHamzBISEhqusB6q6nKjGqBftFnXubtNXP1vJNwm4aEQQviJqkyPt73yyitcdtll9O3bt973Kioq4tixY3z22Wf079+f/v378+GHHzZAKetGqxxBJ6ubN0+S6RFCCD+had4Penbu3MnHH3/Md9991yD3s9ls2Gw2ysrK2LRpExkZGQwcOJCuXbvSs2fPBnmGJzRNgp7mTDI9QgjhJxoj07NhwwYyMjLo3Lkz7du356effmLixInMnTu3TveLjo4mNDSUW2+9FYD27dtz2WWXsXnz5oYsttsasnlLND4JeoQQwk/ouub1Pj2TJk0iOzubjIwMMjIyGDBgAAsWLGDSpEl1vufNN9/MypUrAThx4gQ///wzKSkpDVVkj2iuqEcyPc2RBD1CCOEnNJ1G6dNzLjNmzKBNmzb8+OOP3H333bRp04a8vLxaj82aNYuVK1fSo0cPrrzySqZMmXLejs9eVfmtKTFP86QpaZh0KSoqIiIigsLCQsLDw5u6OEII0aDS1x3h+4/2MunNwU1dlGZLORT/um8tg2/rSvJlrZq6OKKSu9/fkukRQgg/0Vijty5oVdMySzU2SxL0CCGEn9B1zTn7siT460zTNNCkDpsrCXqEEMJPaNIfpUFomiZ12EzVK+iZPXs2mqbxyCOPuPYtWLCAQYMGER4ejqZpFBQU1HqfuXPnkpKSQnh4OOHh4aSmpvLVV19VO2fQoEFomlbt59577612zuHDhxk9ejTBwcG0aNGCv/71r9hsDbGcnhBCNH+uifWkiateNE3qsLmq8+SEmzdvZv78+WcNGywtLWXkyJGMHDmSqVOnunWvNm3aMHv2bDp37oxSinfeeYcxY8awbds2unfv7jrvnnvuYfr06a7t4OBg1992u53Ro0eTkJDADz/8QHZ2NuPHj8dkMjFz5sy6vqYQQlwwXBPryRd2vWi6Js1bzVSdgp5Tp04xbtw43nrrLWbMmFHtWFXWZ926dW7f749//GO17RdffJG5c+fy008/VQt6goODz7nmyurVq/n111/5+uuviY+Pp1evXrzwwgtMmTKF559/3rVmy5ksFgsWi8W1XVRU5HaZhRCiudErMz0OCXrqxZnpaepSiLqoU/PW/fffz+jRoxk2bFhDlwe73c6SJUsoKSkhNTW12rEPPviA2NhYevTowdSpUyktLXUd+/HHH7n44ouJj4937bvqqqsoKipi165dNT5r1qxZREREuH7atm3b4O8jhBC+Qpq3GogmmZ7myuNMz5IlS9i6dWuDTwGenp5Oamoq5eXlhIaGsnz5cpKTk13Hb7nlFtq1a0erVq3YsWMHU6ZMYc+ePXzyyScA5OTkVAt4ANd2Tk5Ojc+cOnUqjz32mGu7qKhIAh8hxAVLdwU9TVyQZq5qUmbR/HgU9GRmZvLwww+zZs0aAgMDG7QgXbp0IS0tjcLCQpYtW8aECRNYv369K/CZOHGi69yLL76Yli1bMnToUPbv30+nTp3q9MyAgAACAgIapPxCCOHrqkZvSfNW/WjICLjmyqPmrS1btpCbm0ufPn0wGo0YjUbWr1/Pa6+9htFoxG6317kgZrOZpKQk+vbty6xZs+jZsydz5sw55/n9+/cHYN++fQAkJCRw7NixaudUbZ+rH5AQQvgTV/OWfGPXj6bJ5ITNlEdBz9ChQ0lPTyctLc31c8kllzBu3DjS0tIwGAwNVjCHw1Gtk/HvpaWlAdCyZUsAUlNTSU9PJzc313XOmjVrCA8Pr9ZMJoQQ/kr69DQMTSYnbLY8at4KCwujR48e1faFhIQQExPj2p+Tk0NOTo4rA5Oenk5YWBiJiYlER0cDzuBp7NixPPDAA4Czb82oUaNITEykuLiYxYsXs27dOlatWgXA/v37Wbx4MVdffTUxMTHs2LGDRx99lCuvvNI1ZH7EiBEkJydz22238dJLL5GTk8MzzzzD/fffL01YQggB6JqM3hL+rc7z9JzLvHnzmDZtmmv7yiuvBGDRokXcfvvtgDOIOX78uOuc3Nxcxo8fT3Z2NhEREaSkpLBq1SqGDx8OOJu+vv76a/75z39SUlJC27ZtueGGG3jmmWdc9zAYDHz55ZdMmjSJ1NRUQkJCmDBhQrV5fYQQwp+5ZmSWjsx1ZrU4u3FIpqd5klXWzyCrrAshLmRH/3uST1/ZxrhpA4iMD679AgE4A5xDO/PZuuoQ2fsKAbj8xs70HCqjfX2Fu9/fDZ7pEUII4ZukI7NnHA7F/q25bFl5iPwjp4jvEM6Q8V0JjQykZeeIpi6eqAMJeoQQwk/IjMzusdsc7NmUw9ZVhyjMLaNttyiueLQ3rS6KdC3lIZonCXqEEMJPnF57q4kL4qNO5pSwb0suv36fxamTFjr2imP4nd2Jby/dHS4UEvQIIYSfON2RWTI9Zzqy5yQ/frKP3EPFmAINdOodR+/h7YhuFdLURRMNTIIeIYTwE9Kn52ybPj/ALysySOgYzqi/XExij2iMpoabc074Fgl6hBDCT0ifnur2/nKMX1Zk0P/ajvQd1U766/iBOq2yLoQQovmRPj2nncgq4dv3dtP50ngJePyIBD1CCOEnpE+PU0WZja/mpxMeE8jgW7tKwONHJOgRQgg/oRtk7S2lFN+8+xulhRZG/eViTAHSf8efSNAjhBB+QpO1t9i66hAHtuUx9PZkmZXaD0nQI4QQfsLfV1k/kJbHT58d4JKr29OxV1xTF0c0ARm9JYQQfsJfR2857A7Svsnkp08P0KlXHP2u6dDURRJNRIIeIYTwE6fn6WnigjSirL0FfLdkD/lZJfQalkjq2E6uehD+R4IeIYTwE/40estmtfPdkv/y28ZsWrQP58YnL6FFO1lOwt9J0COEEH7i9Dw9F3bQYym1smJuOscyivjDLV3ofnkrye4IQIIeIYTwG/7Qp8dmtfP5nDQK88oY83AvWiZFNnWRhA+R0VtCCOEn/GHtrZ+WH+D40VOMeaS3BDziLBL0CCGEnzjdp6dpy+Eth3fls/3bTAaOTSIuMaypiyN8kAQ9QgjhJy7keXrKiiv45p3faJscTcrgNk1dHOGjJOgRQgg/oV+gMzIrh3NpCYdDMXRCN+m0LM5Jgh4hhPATmq6BduFlerauPsSh9HyGTuhGSERAUxdH+DAJeoQQwo/omnZBBT1H95xk02cH6DuqHe0vjm3q4ggfJ0GPEEL4EU3XcFwgHZlLCi2sWriLVhdF0e+PHZu6OKIZkKBHCCH8iKZfGM1bDruDVW/tRNNgxF3dXXMQCXE+EvQIIYQf0XTtgpin58fl+8k5UMRV9/QgONzc1MURzYTMyCyEEH5E17VmPXpLORQbP97H9m8yufzGzrSSCQiFByToEUIIP6I1447MJ3NK+OnTAxzYnseVf76IiwfJfDzCMxL0CCGEH3H26WnqUrhPKcWR3SfZ/k0mh3bmExRmYtTEi+nYO66piyaaIQl6hBDCjzSHPj3Kocg9XMyhnfns35rLiawSYlqHMmR8Ny66NB6DSbqjirqRoEcIIfyIr/bpKTtVwZHdJzm8K59Du05QVlSBOchIYvdorvjTRbS+KBJNkxFaon4k6BFCCD/SmH16lFJYSm2UFVdQVmyt/F1BabGV0qIKSgosnDpZTkmBhbJiKwDRrULoOiCBdj1iSOgUgcEgWR3RcCToEUIIP6IZNI/69NhtDvb+cozcg0WUFlspP1VB2SkrlhIrmq6hGzTnb12rbDpzNk9Zy22UnbLisFcPsHRdIzDMRHC4mdDIAOI7RBAaaSY8NohWnSMJjQps4DcW4jQJeoQQwo/ounuZHqUUB7bl8cMn+yjKLye6ZQjB4WaCw83EtA7FHGwE5Vy8VNkVDofzR9c0NIOG0awTHGYmKMxMcLiJoMq/A4KMsiCoaDIS9AghhB/RtNpXWT910sL6f+8hY8dx2vWIYfR9PYluFdJIJRTCeyToEUIIP6LpGge352EptRIaFUhIZADBYWaMZh2j2cDxI8Vs+uwARrOBkX/pQafeLZq6yEI0GAl6hBDCj/QZkcjB7cc5mVPKkd0nKSmw8PsR7N0GtmTgDUkEhpiappBCeIkEPUII4Ue6DGhJlwEtXdsOh8JSasVW4cBWYcccZCQkIqAJSyiE90jQI4QQfkzXNYJCZcFO4R9kAgQhhBBC+AUJeoQQQgjhF+oV9MyePRtN03jkkUdc+xYsWMCgQYMIDw9H0zQKCgpqvc/cuXNJSUkhPDyc8PBwUlNT+eqrr1zHT5w4wYMPPkiXLl0ICgoiMTGRhx56iMLCwmr30TTtrJ8lS5bU5xWFEEIIcYGoc5+ezZs3M3/+fFJSUqrtLy0tZeTIkYwcOZKpU6e6da82bdowe/ZsOnfujFKKd955hzFjxrBt2za6d+9OVlYWWVlZ/P3vfyc5OZlDhw5x7733kpWVxbJly6rda9GiRYwcOdK1HRkZWddXFEIIIcQFRFN1WG731KlT9OnTh3/961/MmDGDXr168c9//rPaOevWrWPw4MGcPHmyToFHdHQ0L7/8MnfddVeNxz/66CNuvfVWSkpKMBqdsZumaSxfvpzrrrvOrWdYLBYsFotru6ioiLZt21JYWEh4eLjHZRZCCCFE4ysqKiIiIqLW7+86NW/df//9jB49mmHDhtW5gOdit9tZsmQJJSUlpKamnvO8qherCnjOLFtsbCz9+vXj7bff5nwx3axZs4iIiHD9tG3btsHeQwghhBC+xePmrSVLlrB161Y2b97coAVJT08nNTWV8vJyQkNDWb58OcnJyTWee/z4cV544QUmTpxYbf/06dMZMmQIwcHBrF69mvvuu49Tp07x0EMP1XifqVOn8thjj7m2qzI9QgghhLjweBT0ZGZm8vDDD7NmzRoCAxt2JdwuXbqQlpZGYWEhy5YtY8KECaxfv/6swKeoqIjRo0eTnJzM888/X+3Ys88+6/q7d+/elJSU8PLLL58z6AkICCAgQCbhEkIIIfyBR81bW7ZsITc3lz59+mA0GjEajaxfv57XXnsNo9GI3W6vc0HMZjNJSUn07duXWbNm0bNnT+bMmVPtnOLiYkaOHElYWBjLly/HZDr/FOn9+/fnyJEj1frtCCGEEMI/eZTpGTp0KOnp6dX23XHHHXTt2pUpU6ZgMBgarGAOh+OsTsZXXXUVAQEBfP75525lmtLS0oiKipJsjhBCCCE8C3rCwsLo0aNHtX0hISHExMS49ufk5JCTk8O+ffsAZ1+dsLAwEhMTiY6OBpzB09ixY3nggQcAZ9+aUaNGkZiYSHFxMYsXL2bdunWsWrUKcAY8I0aMoLS0lPfff5+ioiKKiooAiIuLw2Aw8MUXX3Ds2DEGDBhAYGAga9asYebMmUyePLke1SOEEEKIC0WDr701b948pk2b5tq+8sorAef8ObfffjsA+/fv5/jx465zcnNzGT9+PNnZ2URERJCSksKqVasYPnw4AFu3bmXTpk0AJCUlVXvewYMHad++PSaTiTfffJNHH30UpRRJSUm88sor3HPPPQ39ikIIIYRohuo0T8+Fyt1x/kIIIYTwHe5+f8sq62eoiv+qms6EEEII4fuqvrdry+NI0HOG4uJiAJmrRwghhGiGiouLiYiIOOdxad46g8PhICsri7CwMDRN88ozqiZAzMzMlCa0OpI6rD+pw/qTOqw/qcP6kzp0UkpRXFxMq1at0PVzz8YjmZ4z6LpOmzZtGuVZVSvKi7qTOqw/qcP6kzqsP6nD+pM65LwZnip1WntLCCGEEKK5kaBHCCGEEH5Bgp5GFhAQwHPPPSezRNeD1GH9SR3Wn9Rh/Ukd1p/UoWekI7MQQggh/IJkeoQQQgjhFyToEUIIIYRfkKBHCCGEEH5Bgh4hhBBC+AUJeoQQQgjhFyToaWD//e9/GTNmDLGxsYSHh3P55Zezdu1a1/Ht27dz880307ZtW4KCgujWrRtz5syp9b5bt25l+PDhREZGEhMTw8SJEzl16pQ3X6XJeKsOa7vvhcQbdbhu3To0TavxZ/Pmzd5+pUbnrc8hwH/+8x/69+9PUFAQUVFRXHfddV56i6blrTps3779WZ/B2bNne/NVmow3P4cAFouFXr16oWkaaWlpXngD3yJBTwO75pprsNlsfPvtt2zZsoWePXtyzTXXkJOTA8CWLVto0aIF77//Prt27eLpp59m6tSpvPHGG+e8Z1ZWFsOGDSMpKYlNmzaxcuVKdu3axe23395Ib9W4vFGH7tz3QuKNOhw4cCDZ2dnVfu6++246dOjAJZdc0liv1mi89Tn8+OOPue2227jjjjvYvn07Gzdu5JZbbmmMV2p03qpDgOnTp1f7LD744IPefp0m4c06BHjiiSdo1aqVN1/BtyjRYPLy8hSgvvvuO9e+oqIiBag1a9ac87r77rtPDR48+JzH58+fr1q0aKHsdrtr344dOxSg9u7d2zCF9xHeqsO63rc58lYd/l5FRYWKi4tT06dPr1d5fZG36tBqtarWrVur//u//2vQ8voib34O27Vrp1599dWGKqrP8va/yytWrFBdu3ZVu3btUoDatm1bQxTbp0mmpwHFxMTQpUsX3n33XUpKSrDZbMyfP58WLVrQt2/fc15XWFhIdHT0OY9bLBbMZnO1lWODgoIA+P777xvuBXyAt+qwrvdtjrxVh7/3+eefk5+fzx133NEQxfYp3qrDrVu3cvToUXRdp3fv3rRs2ZJRo0axc+dOb7xGk/L253D27NnExMTQu3dvXn75ZWw2W0MW3yd4sw6PHTvGPffcw3vvvUdwcHBDF913NXXUdaHJzMxUffv2VZqmKYPBoFq2bKm2bt16zvM3btyojEajWrVq1TnP2blzpzIajeqll15SFotFnThxQt1www0KUDNnzvTGazQpb9RhXe7bnHmrDs80atQoNWrUqIYork/yRh3++9//VoBKTExUy5YtU7/88ou6+eabVUxMjMrPz/fGazQpb30O//GPf6i1a9eq7du3q7lz56rIyEj16KOPNnTxfYI36tDhcKiRI0eqF154QSml1MGDB/0m0yNBjxumTJmigPP+/Pbbb8rhcKhrr71WjRo1Sn3//fdqy5YtatKkSap169YqKyvrrPump6er2NhY1wfvfD744AMVHx+vDAaDMpvNavLkySo+Pl7Nnj3bG6/c4Jq6Dj29ry9q6jo8U2ZmptJ1XS1btqwhX9HrmroOP/jgAwWo+fPnu/aVl5er2NhYNW/evAZ/X29o6jqsycKFC5XRaFTl5eUN8Ype19R1OGfOHHXZZZcpm82mlJKgR/xObm6u+u233877Y7FY1Ndff610XVeFhYXVrk9KSlKzZs2qtm/Xrl2qRYsW6qmnnvKoLDk5Oaq4uFidOnVK6bquli5dWu/3awxNXYee3NdXNXUdnmn69OkqLi5OVVRU1Pu9GlNT1+G3336rALVhw4Zq+/v16+fxP4Om0tR1WJOdO3cqQO3evbvO79WYmroOx4wZo3RdVwaDwfUDKIPBoMaPH9+g7+prjA3XUHbhiouLIy4urtbzSktLAar1vanadjgcru1du3YxZMgQJkyYwIsvvuhRWeLj4wF4++23CQwMZPjw4R5d31Saug7dva8va+o6rKKUYtGiRYwfPx6TyeT2db6gqeuwb9++BAQEsGfPHi6//HIArFYrGRkZtGvXzpNXaTJNXYc1SUtLQ9d1WrRoUafrG1tT1+Frr73GjBkzXNtZWVlcddVVfPjhh/Tv39/d12iemjrqupDk5eWpmJgYdf3116u0tDS1Z88eNXnyZGUymVRaWppSypl+jIuLU7feeqvKzs52/eTm5rrus2nTJtWlSxd15MgR177XX39dbdmyRe3Zs0e98cYbKigoSM2ZM6fR39HbvFWH7tz3QuHNz6FSzqwZlen3C5U36/Dhhx9WrVu3VqtWrVK7d+9Wd911l2rRooU6ceJEo7+nN3mrDn/44Qf16quvqrS0NLV//371/vvvq7i4uAsyQ+Htf5erSPOWqLPNmzerESNGqOjoaBUWFqYGDBigVqxY4Tr+3HPP1dh+265dO9c5a9euVYA6ePCga99tt92moqOjldlsVikpKerdd99txLdqXN6qw9rueyHxVh0qpdTNN9+sBg4c2Ehv0nS8VYcVFRXq8ccfVy1atFBhYWFq2LBhaufOnY34Zo3HG3W4ZcsW1b9/fxUREaECAwNVt27d1MyZM5tNfx5PefPf5Sr+FPRoSinlxUSSEEIIIYRPkHl6hBBCCOEXJOgRQgghhF+QoEcIIYQQfkGCHiGEEEL4BQl6hBBCCOEXJOgRQgghhF+QoEcIIYQQfkGCHiGEEEL4BQl6hBBCCOEXJOgRQgghhF+QoEcIIYQQfuH/A9NCHSx+kgZRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"which VTDs adjoin Sandusky Bay\")\n",
    "for c in [21, 61]: #[21, 61]\n",
    "    for v in countyTractList[c]:\n",
    "        if isRookAdj(tractGeom[3730],tractGeom[v]):\n",
    "        #if tractCPx[v]< -82.5 and tractCPx[v] > -83.1 and tractCPy[v] > 41.41:\n",
    "            for u in range(nUnits):\n",
    "                if allUnits[u] == v:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    plotCenter(u,unitGeom[u],8)\n",
    "plt.show()\n",
    "for c in [21, 61]: #[21, 61]\n",
    "    for v in countyTractList[c]:\n",
    "        if isRookAdj(tractGeom[1008],tractGeom[v]):\n",
    "        #if tractCPx[v]< -82.5 and tractCPx[v] > -83.1 and tractCPy[v] > 41.41:\n",
    "            for u in range(nUnits):\n",
    "                if allUnits[u] == v:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    plotCenter(u,unitGeom[u],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cdaeea8d-27b9-42ea-963a-61032fcf16cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitCP[u].distance(unitCP[1145]) < 0.2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitCP[u])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "dddd99fa-6b55-4028-9275-68cd39c5de2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNLIKE for CONGRESSIONAL OHIO: - Danbury and Bay View are NOT connected across the bridge to KISS.\n"
     ]
    }
   ],
   "source": [
    "#connectors = [1145,4123]\n",
    "#for i in range(len(connectors)):\n",
    "#    for ii in range(len(connectors)):\n",
    "#        if i != ii:\n",
    "#            unitNbrs[connectors[i]].append(connectors[ii])\n",
    "print(\"UNLIKE for CONGRESSIONAL OHIO: - Danbury and Bay View are NOT connected across the bridge to KISS.\")\n",
    "#print(\"... units\",connectors,\"after running the blocks to make the HDs contiguous.  The unit topology file is now updated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cb4277b4-d6ef-4c8c-aa23-6fde227379ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e79873ab-0b8e-48a9-b4b0-05b3135b9cc3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAPexterior):  #should always be the case\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAPexterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")                \n",
    "    \n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "297f4949-f308-4cf3-b3dc-6a3ba5e7b456",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing the Sandusky Bay hole\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"visualizing the Sandusky Bay hole\")\n",
    "for u in countyUnitList[21] + countyUnitList[61]:\n",
    "    if unitCP[u].y > 41.435 and unitCP[u].x < -82.65:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitCP[u],8)\n",
    "        if u in borderUnits:\n",
    "            plotPoly(unitGeom[u],3)\n",
    "    plotPoly(tractGeom[1008],0.1)\n",
    "    plotPoly(tractGeom[3730],0.1)\n",
    "    plotCenter(1008, tractGeom[1008])\n",
    "    plotCenter(3730, tractGeom[3730])\n",
    "#plotPoly(unitGeom[1098],2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7515a610-c549-41f7-b77c-18392ababf36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there is a hole in the border because two unpopulated lakeshore tracts confuse our border algorithm\n",
      "we should add [1062, 3991, 3995, 3996, 3998, 3999, 4000, 4010, 4123, 4133, 4135, 5980, 6406] to the border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#add lake-touchers to borderlist\n",
    "print(\"there is a hole in the border because two unpopulated lakeshore tracts confuse our border algorithm\")\n",
    "newList = list()\n",
    "for u in range(nUnits):\n",
    "    if u not in borderUnits:\n",
    "        if isRookAdj(tractGeom[3730],unitGeom[u]) or isRookAdj(tractGeom[1008],unitGeom[u]):\n",
    "            newList.append(u)\n",
    "print(\"we should add\",newList,\"to the border set\")\n",
    "for u in borderUnits:\n",
    "    if u in countyUnitList[21]+countyUnitList[61]:\n",
    "        plotPoly(unitGeom[u],0.1)\n",
    "for u in newList:\n",
    "    plotPoly(unitGeom[u])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "49568a9c-97d2-44f5-986c-d1ff514d54c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adding these border units back in\n"
     ]
    }
   ],
   "source": [
    "print(\"adding these border units back in\")\n",
    "for u in newList:\n",
    "    if u not in borderUnits:\n",
    "        borderUnits.append(u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "937d923d-9e95-45e9-9aef-77cb4377781d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Try again: checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 420\n"
     ]
    }
   ],
   "source": [
    "print(\"Try again: checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "nLoop = 0\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        nLoop +=1\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        if nLoop < 3:  #avoid excessive printing\n",
    "            for uu in smallPieceList:\n",
    "                plotPoly(unitGeom[uu],0.5)\n",
    "                plotCenter(\"n\"+str(uu),unitGeom[uu])\n",
    "            plotCenter(u,unitGeom[u])\n",
    "            plotPoly(unitGeom[u])\n",
    "            plt.show()\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n",
      "Bueno! no 'border units' that could be eliminated from the border set\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6ef3ef57-2a72-431e-8229-e290979ffe53",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,b in enumerate(borderUnits):  #checking and removing duplicates\n",
    "    for ii, bb in enumerate(borderUnits):\n",
    "        if b == bb and i != ii:\n",
    "            print(i,b)\n",
    "borderUnits = list(set(borderUnits))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "fc1ae6e1-ef52-412b-a966-e9739071af8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For safekeeping, write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"11Sep24\"\n",
    "print(\"For safekeeping, write the unit topology to a file\")  \n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno, \"allUnits\":allUnits} )\n",
    "outname = STATE+str(nDistricts)+\"unitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d4baaea4-7af4-4407-aeb3-f4291d335f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "vtdGeom = tractGeom.copy()  #optional reset if we need to rerun the below clipPoly routine with alternate clipPolys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "abdd07e7-f9be-480a-9d3d-b3948eeaaa2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "toSquishCountiesList = list()   #default; if we did not have any clipPoly's to move in\n",
    "nClips = 0\n",
    "\n",
    "#NOTE :  I HAVE REMOVED SEVERAL CELL BLOCKS FOR STATES WITH SQUISH OPNS; NONE FOR OHIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4ef91f8f-dabb-4c58-a8a4-bba8e725e586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to use shifted shapes (e.g. for MD, MN), else enter 0 for most states to use uncut counties 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on confirming HD center 0 is inside the map's convex hull\n",
      "working on confirming HD center 500 is inside the map's convex hull\n",
      "working on confirming HD center 1001 is inside the map's convex hull\n",
      "working on confirming HD center 1502 is inside the map's convex hull\n",
      "working on confirming HD center 2002 is inside the map's convex hull\n",
      "working on confirming HD center 2502 is inside the map's convex hull\n",
      "working on confirming HD center 3003 is inside the map's convex hull\n",
      "working on confirming HD center 3503 is inside the map's convex hull\n",
      "working on confirming HD center 4004 is inside the map's convex hull\n",
      "working on confirming HD center 4505 is inside the map's convex hull\n",
      "working on confirming HD center 5006 is inside the map's convex hull\n",
      "working on confirming HD center 5506 is inside the map's convex hull\n",
      "working on confirming HD center 6006 is inside the map's convex hull\n",
      "working on confirming HD center 6506 is inside the map's convex hull\n",
      "working on confirming HD center 7006 is inside the map's convex hull\n",
      "working on confirming HD center 7506 is inside the map's convex hull\n",
      "working on confirming HD center 8006 is inside the map's convex hull\n",
      "working on confirming HD center 8507 is inside the map's convex hull\n",
      "Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 - draw polygonal HDs.  #REQUIRES CONSISTENT TREATMENT OF CCB'S \n",
    "#For GA, MA, MD, MN, NC we draw HDs for ALL vtds, even if they are in a CCB.  We use the clipPoly's to squish in the corners for these HD shapes\n",
    "#Not sure if below would work if we have cut districts AND squished-in corners\n",
    "#We normally draw for each (squished or orig) vtd.  Or, read in tract or blockgroup geometries, use those pops and unit centerpoints\n",
    "\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]  #remember, this is after any squish operation\n",
    "print(\"Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\")\n",
    "useSquish = int(input(\"enter 1 to use shifted shapes (e.g. for MD, MN), else enter 0 for most states to use uncut counties\"))\n",
    "if useSquish == 1:\n",
    "    convexMAP = hdCP[countyTractList[uncutCountyList[0]][0]].buffer(0.1) \n",
    "    for v in range(nTracts):\n",
    "        if v not in skipList and v not in allCutVTDs:\n",
    "            convexMAP = convexMAP.union(hdCP[v].buffer(0.1))\n",
    "else:  #build the map via simple union of counties not wholly in fully cut-out districts\n",
    "    convexMAP = countyGeom[uncutCountyList[0]]\n",
    "    for c in uncutCountyList:\n",
    "        convexMAP = convexMAP.union(countyGeom[c])\n",
    "    #convexMAP = MAP.centroid\n",
    "    #for c in range(nCounties):\n",
    "    #    if c not in allFusedCounties:\n",
    "    #        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "popHDlist = list()\n",
    "for t in range(nTracts):\n",
    "    if t not in allCutVTDs and t not in skipList:  #keep low-pop tracts as VEST may have assigned voters to them\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on confirming HD center\",t,\"is inside the map's convex hull\")\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.03))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.01))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "print(\"Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8393e96a-75c6-48ec-94e8-1f6fe58d31df",
   "metadata": {},
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()\n",
    "populatedTractList = popHDlist.copy()  #nomenclature equivalency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bcaf6ea2-1c3b-48c1-9ad6-937d7b7e4385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\n",
      "working on tract-tract distances for unit 0 out of 8941 . Time = 0\n",
      "working on tract-tract distances for unit 400 out of 8941 . Time = 132\n",
      "working on tract-tract distances for unit 800 out of 8941 . Time = 256\n",
      "working on tract-tract distances for unit 1202 out of 8941 . Time = 374\n",
      "working on tract-tract distances for unit 1602 out of 8941 . Time = 453\n",
      "working on tract-tract distances for unit 2002 out of 8941 . Time = 543\n",
      "working on tract-tract distances for unit 2402 out of 8941 . Time = 639\n",
      "working on tract-tract distances for unit 2802 out of 8941 . Time = 731\n",
      "working on tract-tract distances for unit 3203 out of 8941 . Time = 838\n",
      "working on tract-tract distances for unit 3603 out of 8941 . Time = 908\n",
      "working on tract-tract distances for unit 4004 out of 8941 . Time = 989\n",
      "working on tract-tract distances for unit 4404 out of 8941 . Time = 1059\n",
      "working on tract-tract distances for unit 4806 out of 8941 . Time = 1127\n",
      "working on tract-tract distances for unit 5206 out of 8941 . Time = 1191\n",
      "working on tract-tract distances for unit 5606 out of 8941 . Time = 1251\n",
      "working on tract-tract distances for unit 6006 out of 8941 . Time = 1304\n",
      "working on tract-tract distances for unit 6406 out of 8941 . Time = 1345\n",
      "working on tract-tract distances for unit 6806 out of 8941 . Time = 1385\n",
      "working on tract-tract distances for unit 7206 out of 8941 . Time = 1422\n",
      "working on tract-tract distances for unit 7606 out of 8941 . Time = 1452\n",
      "working on tract-tract distances for unit 8006 out of 8941 . Time = 1466\n",
      "working on tract-tract distances for unit 8407 out of 8941 . Time = 1474\n",
      "working on tract-tract distances for unit 8807 out of 8941 . Time = 1480\n",
      "All done; total elapsed  1480 seconds for OH with 99 districts to map\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "our maxD was 2.2\n"
     ]
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT, despite the \"unit\" nomenclature\n",
    "print(\"Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "uuDist = [[int(maxD+1) for t in range(nHDs)] for t in range(nHDs)] #default: too big to capture\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)\n",
    "startTime = time.time()\n",
    "\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on tract-tract distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "                dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "plt.hist(uuDist)\n",
    "plt.show()\n",
    "print(\"our maxD was\",maxD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2f392acb-ba52-4343-80ae-a58ba0b57366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 0 out of 8941 . Time = 0\n",
      "working on unit-unit angles for unit 400 out of 8941 . Time = 67\n",
      "working on unit-unit angles for unit 800 out of 8941 . Time = 131\n",
      "working on unit-unit angles for unit 1202 out of 8941 . Time = 189\n",
      "working on unit-unit angles for unit 1602 out of 8941 . Time = 229\n",
      "working on unit-unit angles for unit 2002 out of 8941 . Time = 276\n",
      "working on unit-unit angles for unit 2402 out of 8941 . Time = 324\n",
      "working on unit-unit angles for unit 2802 out of 8941 . Time = 371\n",
      "working on unit-unit angles for unit 3203 out of 8941 . Time = 422\n",
      "working on unit-unit angles for unit 3603 out of 8941 . Time = 456\n",
      "working on unit-unit angles for unit 4004 out of 8941 . Time = 497\n",
      "working on unit-unit angles for unit 4404 out of 8941 . Time = 533\n",
      "working on unit-unit angles for unit 4806 out of 8941 . Time = 568\n",
      "working on unit-unit angles for unit 5206 out of 8941 . Time = 600\n",
      "working on unit-unit angles for unit 5606 out of 8941 . Time = 629\n",
      "working on unit-unit angles for unit 6006 out of 8941 . Time = 657\n",
      "working on unit-unit angles for unit 6406 out of 8941 . Time = 678\n",
      "working on unit-unit angles for unit 6806 out of 8941 . Time = 698\n",
      "working on unit-unit angles for unit 7206 out of 8941 . Time = 716\n",
      "working on unit-unit angles for unit 7606 out of 8941 . Time = 730\n",
      "working on unit-unit angles for unit 8006 out of 8941 . Time = 737\n",
      "working on unit-unit angles for unit 8407 out of 8941 . Time = 742\n",
      "working on unit-unit angles for unit 8807 out of 8941 . Time = 745\n",
      "All done with angles; total elapsed seconds = 745\n"
     ]
    }
   ],
   "source": [
    "#continuing option 2 \n",
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "            angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "            uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "#plt.hist(uuAngle)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4cc54eba-ca37-4680-9699-37332133afb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119186.343 99 11799448.0 99.0 11799448.0\n",
      "11799448.0 11799448 1.0\n"
     ]
    }
   ],
   "source": [
    "print(r3(aDP), nDistricts, statePop, statePop/aDP, trueStatePop)\n",
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / trueStatePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), r5(np.sum(HDweight))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dac2348a-060a-40a9-beeb-02ff70ec1aa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUN FOR ALL STATES.for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"RUN FOR ALL STATES.for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\")\n",
    "opt2nbrCtyList = [neighborCountyList[c].copy() for c in range(nCounties)]\n",
    "for i in range(nClips):\n",
    "    cList = toSquishCountiesList[i]\n",
    "    for c in cList:\n",
    "        for cc in cList:\n",
    "            for ccc in neighborCountyList[cc]:\n",
    "                if ccc not in opt2nbrCtyList[c] and ccc != c:\n",
    "                    opt2nbrCtyList[c].append(ccc)\n",
    "#for c in range(nCounties):\n",
    "#    for cc in opt2nbrCtyList[c]:\n",
    "#        plotPoly(countyGeom[cc])\n",
    "#    plotCenter(c,countyGeom[c])\n",
    "#    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f2df2e96-2231-433b-a5c7-9790e8d65803",
   "metadata": {},
   "outputs": [],
   "source": [
    "pi=3.1415926536\n",
    "levelL, maxAngleRatio, maxAngle = 0.36, 1.9, 1.8*pi/2.  #original\n",
    "levelL, maxAngleRatio, maxAngle = 0.36, 1.5, 1.5*pi/2.  #2nd try 9-13-24, don't make adjusted angles quite so wide\n",
    "levelL, maxAngleRatio, maxAngle = 0.6, 1.5, 1.5*pi/2.   #3rd try -- adjust angles for more hdCP's not so close to boundary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3873aaa2-0c39-4ac0-99c3-92dd7c18c555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 1056.588 of targetWedgePop\n",
      "I am working on HD number 0 of 8941 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 400 of 8941 HDs.  Elapsed sec = 50\n",
      "I am working on HD number 800 of 8941 HDs.  Elapsed sec = 99\n",
      "I am working on HD number 1202 of 8941 HDs.  Elapsed sec = 159\n",
      "I am working on HD number 1602 of 8941 HDs.  Elapsed sec = 209\n",
      "I am working on HD number 2002 of 8941 HDs.  Elapsed sec = 259\n",
      "I am working on HD number 2402 of 8941 HDs.  Elapsed sec = 308\n",
      "I am working on HD number 2802 of 8941 HDs.  Elapsed sec = 364\n",
      "I am working on HD number 3203 of 8941 HDs.  Elapsed sec = 427\n",
      "I am working on HD number 3603 of 8941 HDs.  Elapsed sec = 476\n",
      "I am working on HD number 4004 of 8941 HDs.  Elapsed sec = 549\n",
      "I am working on HD number 4404 of 8941 HDs.  Elapsed sec = 613\n",
      "I am working on HD number 4806 of 8941 HDs.  Elapsed sec = 677\n",
      "I am working on HD number 5206 of 8941 HDs.  Elapsed sec = 739\n",
      "I am working on HD number 5606 of 8941 HDs.  Elapsed sec = 789\n",
      "I am working on HD number 6006 of 8941 HDs.  Elapsed sec = 843\n",
      "I am working on HD number 6406 of 8941 HDs.  Elapsed sec = 892\n",
      "I am working on HD number 6806 of 8941 HDs.  Elapsed sec = 944\n",
      "I am working on HD number 7206 of 8941 HDs.  Elapsed sec = 1015\n",
      "I am working on HD number 7606 of 8941 HDs.  Elapsed sec = 1086\n",
      "I am working on HD number 8006 of 8941 HDs.  Elapsed sec = 1131\n",
      "I am working on HD number 8407 of 8941 HDs.  Elapsed sec = 1183\n",
      "I am working on HD number 8807 of 8941 HDs.  Elapsed sec = 1227\n",
      "1241.16 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\n",
      "vtd avg and sd usage are 0.9975 0.10969\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",r3(tolerPops[0]),\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "#levelL = 0.36 #SEE ABOVE BLOCK #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "#maxAngleRatio = 1.9  #SEE ABOVE BLOCK #for tightening tract weighting near boundaries  #HD2\n",
    "#maxAngle = 1.8*pi/2. #SEE ABOVE BLOCK  # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "for t in populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "\n",
    "for kkkj, t in enumerate(populatedTractList):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    opt2nbrCtyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"vtd avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in populatedTractList],[HDvPop[t] for t in populatedTractList] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b7f36a0f-a3c6-4b07-aecd-9e010b02918f",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATE = \"14Sep\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "37c238bb-b340-431b-ad11-95e4409d9699",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the polygon-based results to a file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "print(\"Now we will write the polygon-based results to a file\")\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea, \"countyNo\":countyNo,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList} ) \n",
    "\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Bnopatch\"+str(int(nDistricts))+\"nW4.csv\"  #solveWedgeB\n",
    "outname = STATE+str(int(nHDs))+\"convexHD2_\"+str(int(nDistricts))+\"nW4_\"+DATE+\".csv\"  #solveWedgeC\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Buutpatch\"+str(int(nDistricts))+\"nW4.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "075013a3-f3ee-4a44-ac4a-27a7492e027a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./2024state_HD_output/OH8941convexHD2_99nW4_14Sep.csv\n"
     ]
    }
   ],
   "source": [
    "print(\"./\"+outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "6e7e8ccd-8e11-4d6d-970d-b059401e0a3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the original HD polygon shape assignment csv, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv ./2024state_HD_output/OH8941convexHD2_99nW4_14Sep.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read back in the original HD shapes\n",
      "Ready to capture unit centroids based on these HD poly shapes; see next block\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 --> 1 - we already have an ensemble of HDpoly's and their weights (from running option 2 above)\n",
    "#read them in\n",
    "#determine each cluster's required area fraction capture to get 1.00 cluster use across the HD set\n",
    "#then determine total pop (including cluster in/out) for each HD, and total unit use\n",
    "#then move into triage\n",
    "infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                   \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "shapeDF = pd.read_csv(infilename)\n",
    "HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "HDarea = shapeDF['HDarea'].to_list()\n",
    "#tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "nHDs = len(shapeDF)\n",
    "hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "hdCPy = shapeDF['centroid y'].to_list()\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "HDradius0 = shapeDF['HDradius0'].to_list()\n",
    "HDradius1 = shapeDF['HDradius1'].to_list()\n",
    "HDradius2 = shapeDF['HDradius2'].to_list()\n",
    "HDradius3 = shapeDF['HDradius3'].to_list()\n",
    "HDangle0 = shapeDF['HDangle0'].to_list()\n",
    "HDangle1 = shapeDF['HDangle1'].to_list()\n",
    "HDangle2 = shapeDF['HDangle2'].to_list()\n",
    "HDangle3 = shapeDF['HDangle3'].to_list()\n",
    "HDradius, HDangle = list(), list()\n",
    "for t in range(nHDs):\n",
    "    HDradius.append( [HDradius0[t],HDradius1[t],HDradius2[t],HDradius3[t] ] )\n",
    "    HDangle.append(  [HDangle0[t], HDangle1[t], HDangle2[t], HDangle3[t]  ] )\n",
    "print(\"I have read back in the original HD shapes\")\n",
    "popHDlist = list()\n",
    "HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)\n",
    "        HDpoly[t] = buildArcPoly(hdCP[t],HDradius[t], HDangle[t], xScale)\n",
    "if \"countyNo\" in shapeDF.columns.values:  #\"pigs\" == \"can fly\":\n",
    "    HDcountyNo = shapeDF['countyNo'].to_list()\n",
    "else:\n",
    "    print(\"County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\")\n",
    "    HDcountyNo = [-999]*nHDs\n",
    "    for t in popHDlist:\n",
    "        for c, geom in enumerate(countyGeom):\n",
    "            if geom.contains(hdCP[t]):\n",
    "                HDcountyNo[t] = c\n",
    "                break\n",
    "    unassignedList = list()\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] == -999:\n",
    "            unassignedList.append(t)\n",
    "    if len(unassignedList) > 0:\n",
    "        print(\"I found\",len(unassignedList),\"populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\")\n",
    "        for t in unassignedList:\n",
    "            minDist = maxD\n",
    "            for c in range(nCounties):\n",
    "                dist = countyGeom[c].distance(hdCP[t])\n",
    "                if dist < minDist:\n",
    "                    minDist = dist\n",
    "                    HDcountyNo[t] = c\n",
    "    if len(unassignedList) > 0:\n",
    "        if len(unassignedList) > 20:\n",
    "            print(\"  (too many to plot individually)\")\n",
    "        else:\n",
    "            print(\"FYI, here are those manually assigned HDcenters to their counties\")\n",
    "            for t in unassignedList:\n",
    "                print(t,HDweight[t],\" is the HD row and its weight in the ensemble\")\n",
    "                plotPoly(hdCP[t].buffer(0.1))\n",
    "                plotCenter(int(HDvPop[t]),hdCP[t],6)\n",
    "                plotPoly(countyGeom[HDcountyNo[t]])\n",
    "                plotPoly(MAP,0.2)\n",
    "                plt.show()\n",
    "print(\"Ready to capture unit centroids based on these HD poly shapes; see next block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3f0f8434-5f7a-41d1-bd86-07ec230b4aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now, let me renormalize the HDweights if there were cut districts\n",
      "our HDweight sum was 0.9999999999997317 but is now 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"now, let me renormalize the HDweights if there were cut districts\")\n",
    "sumWt = np.sum(HDweight)\n",
    "HDweight = [HDweight[t]/sumWt for t in range(nHDs) ]\n",
    "\n",
    "print(\"our HDweight sum was\",sumWt,\"but is now\",np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af22eba0-8e05-43e7-b96b-6aedab016957",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see early states for continuing w/option 1.  NC and later use option 3 ....\n",
    "#if this is a cold restart, would need to read in unit topology before below block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "8d5f678f-c2df-4ee7-bafe-fd75ba2cb42e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\n"
     ]
    }
   ],
   "source": [
    "#OPTION 3 \n",
    "print(\"lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\")\n",
    "HDtractListString = shapeDF[\"HDtractList\"]\n",
    "HDtractList = [ast.literal_eval(HDtractListString[t]) for t in range(nHDs)]\n",
    "nCCBs = len(CCBlist)\n",
    "CCBpopFrac = [[0. for j in range(nCCBs) ]  for t in range(nHDs)]\n",
    "\n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ec034144-b3dc-4b72-9350-6d85cabcb8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\n",
      "working on vtd --> unit conversion for HD 1 last HD had pop 120081.0\n",
      "working on vtd --> unit conversion for HD 1001 last HD had pop 108803.576\n",
      "working on vtd --> unit conversion for HD 2001 last HD had pop 119333.778\n",
      "working on vtd --> unit conversion for HD 3001 last HD had pop 118722\n",
      "working on vtd --> unit conversion for HD 4001 last HD had pop 119182.0\n",
      "working on vtd --> unit conversion for HD 5001 last HD had pop 118336.0\n",
      "working on vtd --> unit conversion for HD 6001 last HD had pop 118727.984\n",
      "working on vtd --> unit conversion for HD 7001 last HD had pop 119165.285\n",
      "working on vtd --> unit conversion for HD 8001 last HD had pop 119009.564\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\" )\n",
    "HDunitList = [ list() for t in range(nHDs) ]\n",
    "for t in range(nHDs):\n",
    "    if t %1000 == 1:\n",
    "        print(\"working on vtd --> unit conversion for HD\",t,\"last HD had pop\",r3( np.sum([unitPop[u] for u in HDunitList[t-1]]) ) )\n",
    "    capturedCountyPop = [0. for c in range(nCounties)]\n",
    "    capturedCCBpop = [0. for i in range(nCCBs)]\n",
    "    for v in HDtractList[t]:\n",
    "        c = HDcountyNo[v]\n",
    "        if c in range(nCounties):  #we assigned countyno = -999 to unpopulated tracts\n",
    "            if c in unitCounties:\n",
    "                capturedCountyPop[c] += tractPop[v]\n",
    "            elif c in allFusedCounties:\n",
    "                for i, L in enumerate(CCBlist):\n",
    "                    if c in L:\n",
    "                        capturedCCBpop[i] += tractPop[v]\n",
    "            else:  #this vtd not part of a unit or fused county.  Could be split into fragments\n",
    "                for u in countyUnitList[c]:\n",
    "                    if unitParentVTDno[u] == v :  #we assign all units = vtd fragments from this HDTL-captured vtd\n",
    "                        HDunitList[t].append(u)\n",
    "    for c in unitCounties:\n",
    "        if capturedCountyPop[c] > 0.5 * countyPop[c]:\n",
    "            HDunitList[t].append(allUnits.index(c+0.5))\n",
    "    for i in range(nCCBs):  #don't yet assign in/out CCBs.  We'll do so in below block\n",
    "        CCBpopFrac[t][i] = capturedCCBpop[i] / CCBpop[i]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "1494814c-7b90-425c-b8b1-3a2006bdeb2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if we are not allowed to use multiple CCB's in a single HD 1\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\")\n",
    "CCBwouldUse = [0. for i in range(nCCBs)]\n",
    "CCBaddCandidates = [list() for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    jNo = allUnits.index(j+0.25)\n",
    "    ccbNbrSet = set(unitNbrs[jNo])\n",
    "    withCCBpopRat = list()\n",
    "    for t in range(nHDs):\n",
    "        if len(ccbNbrSet.intersection(set(HDunitList[t]))) > 0:\n",
    "            CCBwouldUse[j] += HDweight[t] * nDistricts\n",
    "            CCBaddCandidates[j].append(t)\n",
    "            withCCBpopRat.append((np.sum([unitPop[u] for u in HDunitList[t]]) + CCBpop[j]) / aDP )\n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",r3(CCBwouldUse[j]),\"if we added it to all adjoining districts\")\n",
    "    print(\"Here is the histogram of relative district pop with these added\")\n",
    "    plt.hist(withCCBpopRat,histtype=\"step\",label=\"CCB \"+str(j))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "avoidMultiCCBuse = int(input(\"enter 1 if we are not allowed to use multiple CCB's in a single HD\"))  #option by state\n",
    "if avoidMultiCCBuse == 1:\n",
    "    for j in range(nCCBs):  #this block -- preventing HDs from picking up multiple CCB's; instead, pick up the closest one  \n",
    "        jNo = allUnits.index(j+0.25)\n",
    "        for jj in range(j+1, nCCBs):\n",
    "            jjNo = allUnits.index(j+0.25)\n",
    "            for t in CCBaddCandidates[j].copy():\n",
    "                if t in CCBaddCandidates[j]:\n",
    "                    if t in CCBaddCandidates[jj].copy():\n",
    "                        dist_j, dist_jj = hdCP[t].distance(unitCP[jNo]), hdCP[t].distance(unitCP[jjNo])\n",
    "                        if dist_j > dist_jj:\n",
    "                            CCBaddCandidates[j].remove(t)  #for MN, don't allow any districts with two CCB's\n",
    "                            CCBwouldUse[j] -= HDweight[t] * nDistricts\n",
    "                        else:\n",
    "                            CCBaddCandidates[jj].remove(t)\n",
    "                            CCBwouldUse[jj] -= HDweight[t] * nDistricts\n",
    "for j in range(nCCBs):    \n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",CCBwouldUse[j],\"if we added it to all adjoining districts with no 2-CCB districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0d38eb0e-b516-4fd4-bb9e-6ca0eecfa772",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\")\n",
    "#previously, we went from most-used to least-used CCB vtds until each has unit use\")\n",
    "minCCBuse = 1.02\n",
    "if nCCBs > 0: #skip if not\n",
    "    print(\"currently, we force each CCB's unit use to be\",minCCBuse)\n",
    "    minCCBuse = float(input(\"enter updated value, e.g. 1.02 to hedge against later losses w/contiguity patching\"))\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "CCBuse = [0. for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    postCCBaddPops = list()\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    #ccbNbrSet = set(unitNbrs[unitNo])\n",
    "    #ccbFrac = [-1.*CCBpopFrac[t][j] for t in range(nHDs) ]  # -1 to go from most- to least-used\n",
    "    #idx = np.argsort(ccbFrac)\n",
    "    preCCBpop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in CCBaddCandidates[j]]\n",
    "    idx = np.argsort(preCCBpop)\n",
    "    i = 0\n",
    "    t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "    while CCBuse[j] < minCCBuse  and i < len(CCBaddCandidates[j]) :  #and CCBpopFrac[t][j] > 0:\n",
    "        t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        HDunitList[t].append(unitNo)\n",
    "        postCCBaddPops.append(np.sum([unitPop[u] for u in HDunitList[t] ]))\n",
    "        i +=1\n",
    "    unitUse[unitNo] = CCBuse[j]\n",
    "    print(\"final unit use of CCB\",j, \"is\",r5(CCBuse[j]),\"; here is a histogram of HDpop using this CCB\")\n",
    "    plt.hist(postCCBaddPops)\n",
    "    plt.axvline(aDP,ls=\"--\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf747d9c-cf6d-420e-9c3c-6723619586ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#end of preliminaries for \"option 3\" - converting HDtractlists to unit lists instead of option 2's geometric probing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26102ca2-79c1-48db-b873-7fa3a1553911",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "dc26d08c-a5d9-4e7e-9da5-ae98cf658a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the histogram of HD pops relative to aDP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orig unit avg and sd usage are 0.99751 0.1097\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs) ]\n",
    "print(\"Here is the histogram of HD pops relative to aDP\")\n",
    "plt.hist([HDvPop[t] / aDP for t in range(nHDs) ], bins=20, weights = HDweight, label= \"HDpop / target\" )\n",
    "plt.show()\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53e4c298-e6cb-4fee-a511-b8f6c3940539",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c4183011-724a-4014-8c94-f9f9382c7e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Did I grossly overuse any units, or skip any live ones?\n",
      "no grossly underused\n",
      "above: underused < 0.5; below overused > 1.6\n",
      "no grossly overused\n"
     ]
    }
   ],
   "source": [
    "print(\"Did I grossly overuse any units, or skip any live ones?\")\n",
    "nUnder = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < 0.5 and unitPop[u] > 5:\n",
    "        nUnder +=1\n",
    "        plotPoly(unitGeom[u])\n",
    "        #print(u,unitPop[u], unitUse[u])\n",
    "        #for c in range(nCounties):\n",
    "        #    if u in countyUnitList[c]:\n",
    "        #        print(u,\"is in county\",c)\n",
    "        #        print(\"its neighbor units are\",unitNbrs[u])\n",
    "if nUnder == 0:\n",
    "    print(\"no grossly underused\")\n",
    "else:\n",
    "    plotPoly(MAP,0.2)\n",
    "    plt.show()\n",
    "print(\"above: underused < 0.5; below overused > 1.6\")\n",
    "nOver = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 1.6:\n",
    "        nOver +=1\n",
    "        plotPoly(unitGeom[u],0.3)\n",
    "if nOver == 0:\n",
    "    print(\"no grossly overused\")\n",
    "else:\n",
    "    plotPoly(MAP)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "ce9292b0-7d6a-419a-9491-d0def779ad04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 8934 populated HDs out of 8941\n"
     ]
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs out of\",nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f7f36559-7f1d-4203-8b24-0e8cc2ce1753",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": 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FqVfAHDhwQFlZWZo1a5batWvnbvf5fHr99df14osv6rrrrlOvXr30l7/8RWvXrtW6deskSe+++662bt2qN954Q5dffrluuOEGPfXUU5o+fboOHz4sSZo5c6aSkpL0wgsvqHv37srNzdWtt96ql1566SycMgAAsK5eAZOTk6OMjAylpaUFbC8rK9ORI0cCtnfr1k2dO3dWSUmJJKmkpEQ9evRQXFycuyY9PV1+v19btmxx13z/udPT093nOJHq6mr5/f6ABwAAaJnCgj1g3rx52rhxozZs2HDcPq/Xq/DwcMXExARsj4uLk9frddccGy9H9x/dd6o1fr9f3333naKioo773AUFBXryySeDPR0AAGBQUHdg9uzZoz/84Q+aM2eOIiMjG2qmehk3bpx8Pp/72LNnT1OPBAAAGkhQAVNWVqbKykr17NlTYWFhCgsL06pVqzRt2jSFhYUpLi5Ohw8f1r59+wKOq6ioUHx8vCQpPj7+uN9KOvrx6dZ4PJ4T3n2RpIiICHk8noAHAABomYIKmOuvv17l5eXatGmT++jdu7eysrLc/27durWWLVvmHrNjxw7t3r1bqampkqTU1FSVl5ersrLSXVNcXCyPx6Pk5GR3zbHPcXTN0ecAAAA/bkG9BqZt27a65JJLAra1adNGHTp0cLdnZ2crLy9P7du3l8fj0f3336/U1FT17dtXkjRgwAAlJydr2LBhmjRpkrxerx577DHl5OQoIiJCknTvvffqlVde0ZgxY3TXXXdp+fLlWrBggYqKis7GOQMAAOOCfhHv6bz00ksKDQ1VZmamqqurlZ6erj/+8Y/u/latWmnx4sW67777lJqaqjZt2mj48OGaMGGCuyYpKUlFRUUaPXq0pk6dqvPPP1+vvfaa0tPTz/a4AADAoBDHcZymHqIh+P1+RUdHy+fz8XoYoAW6YKy9O7K7JmY09QhAs3em37/5t5AAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYEFTAFBQW68sor1bZtW8XGxmrw4MHasWNHwJpDhw4pJydHHTp00LnnnqvMzExVVFQErNm9e7cyMjJ0zjnnKDY2Vg8//LBqamoC1qxcuVI9e/ZURESELrroIhUWFtbvDAEAQIsTVMCsWrVKOTk5WrdunYqLi3XkyBENGDBABw8edNeMHj1ab7/9tt58802tWrVKe/fu1S233OLur62tVUZGhg4fPqy1a9dq9uzZKiwsVH5+vrtm586dysjI0LXXXqtNmzZp1KhRuvvuu7V06dKzcMoAAMC6EMdxnPoe/NVXXyk2NlarVq1S//795fP59JOf/ERz587VrbfeKknavn27unfvrpKSEvXt21fvvPOOBg0apL179youLk6SNHPmTD3yyCP66quvFB4erkceeURFRUXavHmz+7mGDBmiffv2acmSJWc0m9/vV3R0tHw+nzweT31PEUAzdcHYoqYeIWi7JmY09QhAs3em379/0GtgfD6fJKl9+/aSpLKyMh05ckRpaWnumm7duqlz584qKSmRJJWUlKhHjx5uvEhSenq6/H6/tmzZ4q459jmOrjn6HAAA4MctrL4H1tXVadSoUbr66qt1ySWXSJK8Xq/Cw8MVExMTsDYuLk5er9ddc2y8HN1/dN+p1vj9fn333XeKioo6bp7q6mpVV1e7H/v9/vqeGgAAaObqfQcmJydHmzdv1rx5887mPPVWUFCg6Oho95GYmNjUIwEAgAZSr4DJzc3V4sWLtWLFCp1//vnu9vj4eB0+fFj79u0LWF9RUaH4+Hh3zfd/K+nox6db4/F4Tnj3RZLGjRsnn8/nPvbs2VOfUwMAAAYEFTCO4yg3N1cLFy7U8uXLlZSUFLC/V69eat26tZYtW+Zu27Fjh3bv3q3U1FRJUmpqqsrLy1VZWemuKS4ulsfjUXJysrvm2Oc4uuboc5xIRESEPB5PwAMAALRMQb0GJicnR3PnztXf//53tW3b1n3NSnR0tKKiohQdHa3s7Gzl5eWpffv28ng8uv/++5Wamqq+fftKkgYMGKDk5GQNGzZMkyZNktfr1WOPPaacnBxFRERIku6991698sorGjNmjO666y4tX75cCxYsUFGRvd86AAAAZ19Qd2BmzJghn8+nX/7yl+rUqZP7mD9/vrvmpZde0qBBg5SZman+/fsrPj5ef/vb39z9rVq10uLFi9WqVSulpqbq9ttv1x133KEJEya4a5KSklRUVKTi4mJddtlleuGFF/Taa68pPT39LJwyAACw7ge9D0xzxvvAAC0b7wMDtEyN8j4wAAAATYGAAQAA5hAwAADAHAIGAACYQ8AAAABzCBgAAGAOAQMAAMwhYAAAgDkEDAAAMIeAAQAA5hAwAADAHAIGAACYQ8AAAABzCBgAAGAOAQMAAMwhYAAAgDkEDAAAMIeAAQAA5hAwAADAHAIGAACYQ8AAAABzCBgAAGAOAQMAAMwhYAAAgDkEDAAAMIeAAQAA5hAwAADAHAIGAACYQ8AAAABzCBgAAGAOAQMAAMwhYAAAgDkEDAAAMIeAAQAA5hAwAADAHAIGAACYQ8AAAABzCBgAAGAOAQMAAMwhYAAAgDkEDAAAMIeAAQAA5hAwAADAHAIGAACYQ8AAAABzCBgAAGAOAQMAAMwhYAAAgDkEDAAAMIeAAQAA5hAwAADAHAIGAACYQ8AAAABzCBgAAGAOAQMAAMwhYAAAgDkEDAAAMIeAAQAA5hAwAADAHAIGAACYQ8AAAABzCBgAAGAOAQMAAMwhYAAAgDkEDAAAMIeAAQAA5hAwAADAHAIGAACY06wDZvr06brgggsUGRmplJQUrV+/vqlHAgAAzUCzDZj58+crLy9P48eP18aNG3XZZZcpPT1dlZWVTT0aAABoYs02YF588UWNGDFCd955p5KTkzVz5kydc845+vOf/9zUowEAgCYW1tQDnMjhw4dVVlamcePGudtCQ0OVlpamkpKSEx5TXV2t6upq92OfzydJ8vv9DTssgCZRV/1tU48QNL4eAad39O+J4zinXNcsA+brr79WbW2t4uLiArbHxcVp+/btJzymoKBATz755HHbExMTG2RGAAhW9JSmngCwY//+/YqOjj7p/mYZMPUxbtw45eXluR/X1dWpqqpKHTp0UEhISBNO1vT8fr8SExO1Z88eeTyeph6nReNaNw6uc+PgOjcOrnMgx3G0f/9+JSQknHJdswyYjh07qlWrVqqoqAjYXlFRofj4+BMeExERoYiIiIBtMTExDTWiSR6Ph78cjYRr3Ti4zo2D69w4uM7/c6o7L0c1yxfxhoeHq1evXlq2bJm7ra6uTsuWLVNqamoTTgYAAJqDZnkHRpLy8vI0fPhw9e7dW3369NGUKVN08OBB3XnnnU09GgAAaGLNNmB++9vf6quvvlJ+fr68Xq8uv/xyLVmy5LgX9uL0IiIiNH78+ON+xIazj2vdOLjOjYPr3Di4zvUT4pzu95QAAACamWb5GhgAAIBTIWAAAIA5BAwAADCHgAEAAOYQMC1UVVWVsrKy5PF4FBMTo+zsbB04cOCMjnUcRzfccINCQkL01ltvNeygxgV7nauqqnT//fera9euioqKUufOnfXAAw+4/3YX/mf69Om64IILFBkZqZSUFK1fv/6U6998801169ZNkZGR6tGjh/7xj3800qS2BXOdZ82apX79+qldu3Zq166d0tLSTvv/Bf8V7J/no+bNm6eQkBANHjy4YQc0iIBpobKysrRlyxYVFxdr8eLFWr16tUaOHHlGx06ZMuVH/88vnKlgr/PevXu1d+9eTZ48WZs3b1ZhYaGWLFmi7OzsRpy6+Zs/f77y8vI0fvx4bdy4UZdddpnS09NVWVl5wvVr167V0KFDlZ2drQ8++ECDBw/W4MGDtXnz5kae3JZgr/PKlSs1dOhQrVixQiUlJUpMTNSAAQP0xRdfNPLktgR7nY/atWuXHnroIfXr16+RJjXGQYuzdetWR5KzYcMGd9s777zjhISEOF988cUpj/3ggw+c8847z/nyyy8dSc7ChQsbeFq7fsh1PtaCBQuc8PBw58iRIw0xpkl9+vRxcnJy3I9ra2udhIQEp6Cg4ITrf/Ob3zgZGRkB21JSUpx77rmnQee0Ltjr/H01NTVO27ZtndmzZzfUiC1Cfa5zTU2Nc9VVVzmvvfaaM3z4cOfmm29uhElt4Q5MC1RSUqKYmBj17t3b3ZaWlqbQ0FCVlpae9Lhvv/1Wt912m6ZPn37Sf3MK/1Pf6/x9Pp9PHo9HYWHN9n0lG9Xhw4dVVlamtLQ0d1toaKjS0tJUUlJywmNKSkoC1ktSenr6Sdejftf5+7799lsdOXJE7du3b6gxzavvdZ4wYYJiY2O5O3sKfMVsgbxer2JjYwO2hYWFqX379vJ6vSc9bvTo0brqqqt08803N/SILUJ9r/Oxvv76az311FNn/OO9H4Ovv/5atbW1x73rdlxcnLZv337CY7xe7wnXn+n/hx+j+lzn73vkkUeUkJBwXDzif+pznd977z29/vrr2rRpUyNMaBd3YAwZO3asQkJCTvk40y8837do0SItX75cU6ZMObtDG9SQ1/lYfr9fGRkZSk5O1hNPPPHDBwca0cSJEzVv3jwtXLhQkZGRTT1Oi7F//34NGzZMs2bNUseOHZt6nGaNOzCGPPjgg/rd7353yjU//elPFR8ff9yLw2pqalRVVXXSHw0tX75cn376qWJiYgK2Z2Zmql+/flq5cuUPmNyWhrzOR+3fv18DBw5U27ZttXDhQrVu3fqHjt1idOzYUa1atVJFRUXA9oqKipNe1/j4+KDWo37X+ajJkydr4sSJ+uc//6lLL720Icc0L9jr/Omnn2rXrl266aab3G11dXWS/nuHd8eOHbrwwgsbdmgrmvpFODj7jr649P3333e3LV269JQvLv3yyy+d8vLygIckZ+rUqc5nn33WWKObUp/r7DiO4/P5nL59+zrXXHONc/DgwcYY1Zw+ffo4ubm57se1tbXOeeedd8oX8Q4aNChgW2pqKi/iPY1gr7PjOM5zzz3neDwep6SkpDFGbBGCuc7ffffdcV+Lb775Zue6665zysvLnerq6sYcvVkjYFqogQMHOldccYVTWlrqvPfee87FF1/sDB061N3/+eefO127dnVKS0tP+hzit5BOK9jr7PP5nJSUFKdHjx7OJ5984nz55Zfuo6ampqlOo9mZN2+eExER4RQWFjpbt251Ro4c6cTExDher9dxHMcZNmyYM3bsWHf9mjVrnLCwMGfy5MnOtm3bnPHjxzutW7d2ysvLm+oUTAj2Ok+cONEJDw93/vrXvwb82d2/f39TnYIJwV7n7+O3kE6MgGmh/u///s8ZOnSoc+655zoej8e58847A77I7Ny505HkrFix4qTPQcCcXrDXecWKFY6kEz527tzZNCfRTL388stO586dnfDwcKdPnz7OunXr3H3XXHONM3z48ID1CxYscH72s5854eHhzs9//nOnqKiokSe2KZjr3KVLlxP+2R0/fnzjD25MsH+ej0XAnFiI4zhOY//YCgAA4Ifgt5AAAIA5BAwAADCHgAEAAOYQMAAAwBwCBgAAmEPAAAAAcwgYAABgDgEDAADMIWAAAIA5BAwAADCHgAEAAOYQMAAAwJz/Bzezsf29OG/6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "27bc7f0a-d7ff-4c68-830f-29ba5013c314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#popHDlist = populatedTractList.copy()  #somehow our popHDlist started to include the cut District HD starters\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "af8a4945-40c2-40df-8dd4-d516f4de2f0f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 32\n",
      "working on HD 1402 time is now 54\n",
      "working on HD 2102 time is now 74\n",
      "working on HD 2802 time is now 99\n",
      "working on HD 3503 time is now 123\n",
      "working on HD 4204 time is now 142\n",
      "working on HD 4906 time is now 167\n",
      "working on HD 5606 time is now 195\n",
      "working on HD 6306 time is now 224\n",
      "working on HD 7006 time is now 256\n",
      "working on HD 7706 time is now 291\n",
      "working on HD 8407 time is now 318\n",
      "out of 8934 total HDs, there were 4143.0 contiguous and 497.0 complement-contiguous HDs\n",
      "4513 HDs had both discontiguity problems, while enclave-only = 3924 and discontig only= 278\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 4791 3924\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "40f06e44-45be-4a36-b62c-26698af70e06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 113227 district pop vs 119186 target\n",
      "we'll also trim any HD with total islands' pop <= 2383\n",
      "working on HD 1\n",
      "working on HD 323\n",
      "working on HD 665\n",
      "working on HD 998\n",
      "working on HD 1567\n",
      "working on HD 2049\n",
      "working on HD 2377\n",
      "working on HD 2703\n",
      "working on HD 3062\n",
      "working on HD 3461\n",
      "working on HD 3918\n",
      "working on HD 4639\n",
      "working on HD 5110\n",
      "working on HD 5484\n",
      "working on HD 5802\n",
      "working on HD 6214\n",
      "working on HD 6661\n",
      "working on HD 6927\n",
      "working on HD 7169\n",
      "working on HD 7423\n",
      "working on HD 7707\n",
      "working on HD 8007\n",
      "working on HD 8351\n",
      "working on HD 8629\n",
      "Out of 4791 discontig HDs 4791 had discontig HDs.\n",
      "Of these, 4251 won't be under 113227 after all minor discontigys were shed, while 540 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "reclassifying HD 1\n",
      "reclassifying HD 838\n",
      "reclassifying HD 2049\n",
      "reclassifying HD 2868\n",
      "reclassifying HD 3918\n",
      "reclassifying HD 5328\n",
      "reclassifying HD 6214\n",
      "reclassifying HD 7050\n",
      "reclassifying HD 7707\n",
      "reclassifying HD 8494\n",
      "There are 540 HDs still with both discontinuity problems\n",
      "Additionally, 4118 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "4037f8cb-661b-4032-bfb9-e147975e3bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 0\n",
      "working on enclave-generating HD 1273\n",
      "working on enclave-generating HD 2125\n",
      "working on enclave-generating HD 3372\n",
      "working on enclave-generating HD 4237\n",
      "working on enclave-generating HD 5050\n",
      "working on enclave-generating HD 6175\n",
      "working on enclave-generating HD 7663\n",
      "working on enclave-generating HD 121\n",
      "working on enclave-generating HD 1143\n",
      "working on enclave-generating HD 2619\n",
      "working on enclave-generating HD 3927\n",
      "working on enclave-generating HD 5513\n",
      "working on enclave-generating HD 6605\n",
      "working on enclave-generating HD 7234\n",
      "working on enclave-generating HD 7971\n",
      "working on enclave-generating HD 8865\n",
      "Out of 8042 HDpolys that generated enclaves, 0 had contiguous adjrs, while 2467 neighbored a state boundary while 5575 had only internal enclaves\n"
     ]
    }
   ],
   "source": [
    "print(\"Classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t)\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "05f929ce-4f94-47a6-baf4-60c5a89e2ae5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 125145 district pop vs 119186 target\n",
      "working on enclave-y HD 7 . We have evaluated 0 of 8042 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 1172 . We have evaluated 200 of 8042 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 1758 . We have evaluated 400 of 8042 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 2822 . We have evaluated 600 of 8042 enclavy HDs.Time is now 1\n",
      "working on enclave-y HD 3578 . We have evaluated 800 of 8042 enclavy HDs.Time is now 1\n",
      "working on enclave-y HD 4257 . We have evaluated 1000 of 8042 enclavy HDs.Time is now 1\n",
      "working on enclave-y HD 4690 . We have evaluated 1200 of 8042 enclavy HDs.Time is now 2\n",
      "working on enclave-y HD 5278 . We have evaluated 1400 of 8042 enclavy HDs.Time is now 2\n",
      "working on enclave-y HD 6026 . We have evaluated 1600 of 8042 enclavy HDs.Time is now 3\n",
      "working on enclave-y HD 6344 . We have evaluated 1800 of 8042 enclavy HDs.Time is now 3\n",
      "working on enclave-y HD 6909 . We have evaluated 2000 of 8042 enclavy HDs.Time is now 4\n",
      "working on enclave-y HD 7821 . We have evaluated 2200 of 8042 enclavy HDs.Time is now 4\n",
      "working on enclave-y HD 8796 . We have evaluated 2400 of 8042 enclavy HDs.Time is now 4\n",
      "working on enclave-y HD 413 . We have evaluated 2600 of 8042 enclavy HDs.Time is now 5\n",
      "working on enclave-y HD 1105 . We have evaluated 2800 of 8042 enclavy HDs.Time is now 6\n",
      "working on enclave-y HD 2257 . We have evaluated 3000 of 8042 enclavy HDs.Time is now 6\n",
      "working on enclave-y HD 2928 . We have evaluated 3200 of 8042 enclavy HDs.Time is now 7\n",
      "working on enclave-y HD 3771 . We have evaluated 3400 of 8042 enclavy HDs.Time is now 7\n",
      "working on enclave-y HD 4910 . We have evaluated 3600 of 8042 enclavy HDs.Time is now 8\n",
      "working on enclave-y HD 5454 . We have evaluated 3800 of 8042 enclavy HDs.Time is now 8\n",
      "working on enclave-y HD 5934 . We have evaluated 4000 of 8042 enclavy HDs.Time is now 9\n",
      "working on enclave-y HD 6432 . We have evaluated 4200 of 8042 enclavy HDs.Time is now 9\n",
      "working on enclave-y HD 6832 . We have evaluated 4400 of 8042 enclavy HDs.Time is now 10\n",
      "working on enclave-y HD 7079 . We have evaluated 4600 of 8042 enclavy HDs.Time is now 10\n",
      "working on enclave-y HD 7325 . We have evaluated 4800 of 8042 enclavy HDs.Time is now 11\n",
      "working on enclave-y HD 7587 . We have evaluated 5000 of 8042 enclavy HDs.Time is now 11\n",
      "working on enclave-y HD 7973 . We have evaluated 5200 of 8042 enclavy HDs.Time is now 12\n",
      "working on enclave-y HD 8505 . We have evaluated 5400 of 8042 enclavy HDs.Time is now 12\n",
      "working on enclave-y HD 168 . We have evaluated 5600 of 8042 enclavy HDs.Time is now 17\n",
      "working on enclave-y HD 1048 . We have evaluated 5800 of 8042 enclavy HDs.Time is now 23\n",
      "working on enclave-y HD 1747 . We have evaluated 6000 of 8042 enclavy HDs.Time is now 23\n",
      "working on enclave-y HD 2624 . We have evaluated 6200 of 8042 enclavy HDs.Time is now 24\n",
      "working on enclave-y HD 3677 . We have evaluated 6400 of 8042 enclavy HDs.Time is now 24\n",
      "working on enclave-y HD 4292 . We have evaluated 6600 of 8042 enclavy HDs.Time is now 24\n",
      "working on enclave-y HD 5148 . We have evaluated 6800 of 8042 enclavy HDs.Time is now 25\n",
      "working on enclave-y HD 8370 . We have evaluated 7000 of 8042 enclavy HDs.Time is now 25\n",
      "working on enclave-y HD 747 . We have evaluated 7200 of 8042 enclavy HDs.Time is now 30\n",
      "working on enclave-y HD 2022 . We have evaluated 7400 of 8042 enclavy HDs.Time is now 31\n",
      "working on enclave-y HD 3263 . We have evaluated 7600 of 8042 enclavy HDs.Time is now 32\n",
      "working on enclave-y HD 6575 . We have evaluated 7800 of 8042 enclavy HDs.Time is now 32\n",
      "working on enclave-y HD 8809 . We have evaluated 8000 of 8042 enclavy HDs.Time is now 33\n",
      "Out of 8042 HDs with enclaves 187 had enclaves.\n",
      "Of these, 0 wouldn't be over 125145 if all enclaves filled, while 187 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(internals + edgers):\n",
    "    if iii%200 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "8987ad1f-d372-415d-a2ec-8fc6736c5102",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5575 2467 4513 988\n",
      "2467 187 0\n"
     ]
    }
   ],
   "source": [
    "print(len(internals),len(edgers),len(doubleTroubleList),len(set(edgers).intersection(set(doubleTroubleList))))\n",
    "print(len(set(edgers).difference(set(canFill))) ,len(cantFill) , len(canFill))\n",
    "#610 3379 2187 1954\n",
    "#80 80 3909  for original MN option3 (assigned CCBs by captured area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "8fa887be-92e6-422a-8e80-e00fd3265aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00671 0.10778\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e9d50ef8-39b2-4e22-a11e-cabeda78721e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "b759b3c7-b8a3-4d2c-a8fe-50fa93b3e940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FRkYqNjZWU6ZMUU1NTVCblStX6pJLLpHH41Hv3r2Vm5t7Zj0EAACtTqMCzKpVq5SVlaU1a9YoPz9f1dXVSk9P1+HDh502kyZN0vvvv6+33npLq1at0t69e3XTTTc502tra5WZmamqqip98sknevXVV5Wbm6tp06Y5bXbu3KnMzEwNGzZMxcXFevDBB3XPPfdo2bJlTdBlALBHVU2dfrN4i36zeIuqaupauhwgZLiMMWd8b+qvvvpKsbGxWrVqlYYOHaqKigp17dpV8+fP18033yxJ2rp1q/r166fCwkINGTJES5Ys0U9/+lPt3btXcXFxkqS5c+dq6tSp+uqrr+R2uzV16lTl5eXpiy++cJZ16623qry8XEuXLj2t2gKBgKKiolRRUSGv13umXQSAFvVtVY2Spx37423z9AxFuk888t/r4bxmW/6umZnNNm+gIaf7+31WVyFVVFRIkmJiYiRJRUVFqq6uVlpamtOmb9++6tGjhwoLCyVJhYWF6t+/vxNeJCkjI0OBQECbNm1y2hw/j/o29fNoSGVlpQKBQNAAAABapzMOMHV1dXrwwQd1xRVX6KKLLpIk+f1+ud1uRUdHB7WNi4uT3+932hwfXuqn1087VZtAIKAjR440WM+MGTMUFRXlDImJiWfaNQAAEOLOOMBkZWXpiy++0BtvvNGU9ZyxnJwcVVRUOMPu3btbuiQAANBMzugy6okTJ2rRokVavXq1unfv7oyPj49XVVWVysvLg/bClJWVKT4+3mmzbt26oPnVX6V0fJvvXrlUVlYmr9eriIiIBmvyeDzyeDxn0h0AAGCZRgUYY4zuv/9+vfPOO1q5cqWSkpKCpqekpKht27YqKCjQqFGjJEklJSUqLS2Vz+eTJPl8Pv3617/W/v37FRsbK0nKz8+X1+tVcnKy02bx4sVB887Pz3fmAQA4NzhBGKGqUQEmKytL8+fP13vvvaeOHTs656xERUUpIiJCUVFRGjdunLKzsxUTEyOv16v7779fPp9PQ4YMkSSlp6crOTlZd9xxh2bNmiW/369HHnlEWVlZzh6UCRMm6Pe//70eeugh3X333VqxYoUWLFigvLzm+yIBAAB7NOoyapfL1eD4efPm6c4775R07EZ2kydP1uuvv67KykplZGRozpw5zuEhSfryyy913333aeXKlWrfvr3Gjh2rmTNnqk2bf+aplStXatKkSdq8ebO6d++uRx991FnG6eAyagCtQV2d0favDkmSenftoLCwE7fDzbmXpDmxBwYNOd3f77O6D0woI8AA+KEgwKA1OSf3gQEAAGgJPMwRAEJYVU2dXvhwuyQpa1hvudvwdycgEWAAIKTV1NXpuYJtkqSf//g8udlxDkjiEBIAALAQAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHW4jBoAQpinTbjey7rCeQ3gGAIMAISw8DCXLk6MbukygJDDISQAAGAd9sAAQAirqqnTvI93SpLuuiKJRwkA/4cAAwAhrKauTjOWbJUk3eHryaMEgP/DNwEAAFiHAAMAAKzDISQAOEd6PZx3Vp9PnrasiSoB7MceGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArNPoALN69Wpdf/31SkhIkMvl0rvvvhs0/c4775TL5QoaRowYEdTmwIEDGj16tLxer6KjozVu3DgdOnQoqM2GDRt01VVXqV27dkpMTNSsWbMa3zsAANAqNTrAHD58WBdffLFeeOGFk7YZMWKE9u3b5wyvv/560PTRo0dr06ZNys/P16JFi7R69Wrde++9zvRAIKD09HT17NlTRUVFmj17th5//HG99NJLjS0XAAC0Qm0a+4Frr71W11577SnbeDwexcfHNzhty5YtWrp0qT799FNdeumlkqTnn39e1113nZ566iklJCTotddeU1VVlV555RW53W5deOGFKi4u1jPPPBMUdAAAwA9Ts5wDs3LlSsXGxqpPnz6677779PXXXzvTCgsLFR0d7YQXSUpLS1NYWJjWrl3rtBk6dKjcbrfTJiMjQyUlJfrmm2+ao2QAAGCRRu+B+T4jRozQTTfdpKSkJO3YsUP/+Z//qWuvvVaFhYUKDw+X3+9XbGxscBFt2igmJkZ+v1+S5Pf7lZSUFNQmLi7OmdapU6cTlltZWanKykrnfSAQaOquAQCAENHkAebWW291Xvfv318DBgzQ+eefr5UrV2r48OFNvTjHjBkz9MQTTzTb/AEAQOho9suozzvvPHXp0kXbt2+XJMXHx2v//v1BbWpqanTgwAHnvJn4+HiVlZUFtal/f7Jza3JyclRRUeEMu3fvbuquAACAENHsAWbPnj36+uuv1a1bN0mSz+dTeXm5ioqKnDYrVqxQXV2dUlNTnTarV69WdXW10yY/P199+vRp8PCRdOzEYa/XGzQAAIDWqdEB5tChQyouLlZxcbEkaefOnSouLlZpaakOHTqkKVOmaM2aNdq1a5cKCgp0ww03qHfv3srIyJAk9evXTyNGjND48eO1bt06ffzxx5o4caJuvfVWJSQkSJJuv/12ud1ujRs3Tps2bdKbb76p5557TtnZ2U3XcwAAYK1GB5jPPvtMgwYN0qBBgyRJ2dnZGjRokKZNm6bw8HBt2LBBI0eO1AUXXKBx48YpJSVFf/3rX+XxeJx5vPbaa+rbt6+GDx+u6667TldeeWXQPV6ioqL0wQcfaOfOnUpJSdHkyZM1bdo0LqEGAACSJJcxxrR0Ec0hEAgoKipKFRUVHE4CEBJ6PZzX0iWElF0zM1u6BISg0/395llIAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1ml0gFm9erWuv/56JSQkyOVy6d133w2abozRtGnT1K1bN0VERCgtLU3btm0LanPgwAGNHj1aXq9X0dHRGjdunA4dOhTUZsOGDbrqqqvUrl07JSYmatasWY3vHQAAaJUaHWAOHz6siy++WC+88EKD02fNmqXf/e53mjt3rtauXav27dsrIyNDR48eddqMHj1amzZtUn5+vhYtWqTVq1fr3nvvdaYHAgGlp6erZ8+eKioq0uzZs/X444/rpZdeOoMuAgCA1sZljDFn/GGXS++8845uvPFGScf2viQkJGjy5Mn65S9/KUmqqKhQXFyccnNzdeutt2rLli1KTk7Wp59+qksvvVSStHTpUl133XXas2ePEhIS9OKLL+pXv/qV/H6/3G63JOnhhx/Wu+++q61bt55WbYFAQFFRUaqoqJDX6z3TLgJAk+n1cF5LlxBSds3MbOkSEIJO9/e7Sc+B2blzp/x+v9LS0pxxUVFRSk1NVWFhoSSpsLBQ0dHRTniRpLS0NIWFhWnt2rVOm6FDhzrhRZIyMjJUUlKib775psFlV1ZWKhAIBA0AAKB1atIA4/f7JUlxcXFB4+Pi4pxpfr9fsbGxQdPbtGmjmJiYoDYNzeP4ZXzXjBkzFBUV5QyJiYln3yEAABCSWs1VSDk5OaqoqHCG3bt3t3RJAACgmTRpgImPj5cklZWVBY0vKytzpsXHx2v//v1B02tqanTgwIGgNg3N4/hlfJfH45HX6w0aAABA69SkASYpKUnx8fEqKChwxgUCAa1du1Y+n0+S5PP5VF5erqKiIqfNihUrVFdXp9TUVKfN6tWrVV1d7bTJz89Xnz591KlTp6YsGQAAWKjRAebQoUMqLi5WcXGxpGMn7hYXF6u0tFQul0sPPvig/uu//ksLFy7Uxo0bNWbMGCUkJDhXKvXr108jRozQ+PHjtW7dOn388ceaOHGibr31ViUkJEiSbr/9drndbo0bN06bNm3Sm2++qeeee07Z2dlN1nEAAGCvNo39wGeffaZhw4Y57+tDxdixY5Wbm6uHHnpIhw8f1r333qvy8nJdeeWVWrp0qdq1a+d85rXXXtPEiRM1fPhwhYWFadSoUfrd737nTI+KitIHH3ygrKwspaSkqEuXLpo2bVrQvWIAAMAP11ndByaUcR8YAKGG+8AE4z4waEiL3AcGAADgXCDAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6bVq6AAAIJb0ezmvpEgCcBgIMAKBFNFdY3DUzs1nmi9DCISQAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDpNHmAef/xxuVyuoKFv377O9KNHjyorK0udO3dWhw4dNGrUKJWVlQXNo7S0VJmZmYqMjFRsbKymTJmimpqapi4VAABYqlnuxHvhhRdq+fLl/1xIm38uZtKkScrLy9Nbb72lqKgoTZw4UTfddJM+/vhjSVJtba0yMzMVHx+vTz75RPv27dOYMWPUtm1b/eY3v2mOcgEAgGWaJcC0adNG8fHxJ4yvqKjQyy+/rPnz5+uaa66RJM2bN0/9+vXTmjVrNGTIEH3wwQfavHmzli9frri4OA0cOFBPPvmkpk6dqscff1xut7s5SgYAABZplnNgtm3bpoSEBJ133nkaPXq0SktLJUlFRUWqrq5WWlqa07Zv377q0aOHCgsLJUmFhYXq37+/4uLinDYZGRkKBALatGnTSZdZWVmpQCAQNAAAgNapyQNMamqqcnNztXTpUr344ovauXOnrrrqKh08eFB+v19ut1vR0dFBn4mLi5Pf75ck+f3+oPBSP71+2snMmDFDUVFRzpCYmNi0HQMAACGjyQ8hXXvttc7rAQMGKDU1VT179tSCBQsUERHR1Itz5OTkKDs723kfCAQIMQAAtFLNfhl1dHS0LrjgAm3fvl3x8fGqqqpSeXl5UJuysjLnnJn4+PgTrkqqf9/QeTX1PB6PvF5v0AAAAFqnZg8whw4d0o4dO9StWzelpKSobdu2KigocKaXlJSotLRUPp9PkuTz+bRx40bt37/faZOfny+v16vk5OTmLhcAAFigyQ8h/fKXv9T111+vnj17au/evXrssccUHh6u2267TVFRURo3bpyys7MVExMjr9er+++/Xz6fT0OGDJEkpaenKzk5WXfccYdmzZolv9+vRx55RFlZWfJ4PE1dLgAAsFCTB5g9e/botttu09dff62uXbvqyiuv1Jo1a9S1a1dJ0rPPPquwsDCNGjVKlZWVysjI0Jw5c5zPh4eHa9GiRbrvvvvk8/nUvn17jR07VtOnT2/qUgEAgKVcxhjT0kU0h0AgoKioKFVUVHA+DIDT1uvhvJYuAWdp18zMli4BZ+F0f795FhIAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFinTUsXAABnotfDeS1dAoAWxB4YAABgHQIMAACwDoeQAACtSnMeXtw1M7PZ5o3GYQ8MAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdbmQHoNnwvCIAzYUAAwDAaWquUM4dfhuPQ0gAAMA6BBgAAGAdDiEB4FwVANYhwAAA0MJ4gnbjcQgJAABYJ6QDzAsvvKBevXqpXbt2Sk1N1bp161q6JAAAEAJC9hDSm2++qezsbM2dO1epqan67W9/q4yMDJWUlCg2Nraly8Nx2PUJADjXQjbAPPPMMxo/frzuuusuSdLcuXOVl5enV155RQ8//HALV9d8OJkSJ8P/DQD4p5AMMFVVVSoqKlJOTo4zLiwsTGlpaSosLGzwM5WVlaqsrHTeV1RUSJICgUDzFtvE6iq/bekSQkqPSW+1dAkAYDXbfgfr6zXGnLJdSAaYf/zjH6qtrVVcXFzQ+Li4OG3durXBz8yYMUNPPPHECeMTExObpUYAAGwQ9duWruDMHDx4UFFRUSedHpIB5kzk5OQoOzvbeV9XV6cDBw6oc+fOcrlczbrsQCCgxMRE7d69W16vt1mXFQrob+v3Q+sz/W3d6K9djDE6ePCgEhISTtkuJANMly5dFB4errKysqDxZWVlio+Pb/AzHo9HHo8naFx0dHRzldggr9dr5X+WM0V/W78fWp/pb+tGf+1xqj0v9ULyMmq3262UlBQVFBQ44+rq6lRQUCCfz9eClQEAgFAQkntgJCk7O1tjx47VpZdeqsGDB+u3v/2tDh8+7FyVBAAAfrhCNsDccsst+uqrrzRt2jT5/X4NHDhQS5cuPeHE3lDg8Xj02GOPnXAIq7Wiv63fD63P9Ld1o7+tk8t833VKAAAAISYkz4EBAAA4FQIMAACwDgEGAABYhwADAACs84MIMKtXr9b111+vhIQEuVwuvfvuu0HTjTGaNm2aunXrpoiICKWlpWnbtm3O9JUrV8rlcjU4fPrpp5KkXbt2NTh9zZo1Qct666231LdvX7Vr1079+/fX4sWLG1VLU/X57bffVnp6unOn4uLi4hPmcfToUWVlZalz587q0KGDRo0adcLNBUtLS5WZmanIyEjFxsZqypQpqqmpCWqzcuVKXXLJJfJ4POrdu7dyc3NPWNYLL7ygXr16qV27dkpNTdW6devOaX8PHDig+++/X3369FFERIR69OihBx54wHmmVr2G1vEbb7xhXX8l6eqrrz6hLxMmTAhq01rW78m+ny6XS2+99c/nbdmwfqurqzV16lT1799f7du3V0JCgsaMGaO9e/cGzePAgQMaPXq0vF6voqOjNW7cOB06dCiozYYNG3TVVVepXbt2SkxM1KxZs06o5Vxss862v7t27dK4ceOUlJSkiIgInX/++XrsscdUVVUV1CZUttFNsX579ep1Ql9mzpwZ1CZU1m+zMT8AixcvNr/61a/M22+/bSSZd955J2j6zJkzTVRUlHn33XfN559/bkaOHGmSkpLMkSNHjDHGVFZWmn379gUN99xzj0lKSjJ1dXXGGGN27txpJJnly5cHtauqqnKW8/HHH5vw8HAza9Yss3nzZvPII4+Ytm3bmo0bN552LU3V5//+7/82TzzxhPnjH/9oJJn169efMI8JEyaYxMREU1BQYD777DMzZMgQc/nllzvTa2pqzEUXXWTS0tLM+vXrzeLFi02XLl1MTk6O0+Zvf/ubiYyMNNnZ2Wbz5s3m+eefN+Hh4Wbp0qVOmzfeeMO43W7zyiuvmE2bNpnx48eb6OhoU1ZWds76u3HjRnPTTTeZhQsXmu3bt5uCggLzox/9yIwaNSqonSQzb968oHV8/Lqxpb/GGPPjH//YjB8/PqgvFRUVzvTWtH5rampO+A4/8cQTpkOHDubgwYNOOxvWb3l5uUlLSzNvvvmm2bp1qyksLDSDBw82KSkpQfMYMWKEufjii82aNWvMX//6V9O7d29z2223OdMrKipMXFycGT16tPniiy/M66+/biIiIswf/vAHp8252madbX+XLFli7rzzTrNs2TKzY8cO895775nY2FgzefJkp00obaObYv327NnTTJ8+Pagvhw4dcqaH0vptLj+IAHO87/5nqaurM/Hx8Wb27NnOuPLycuPxeMzrr7/e4DyqqqpM165dzfTp051x9V+Ohn4o6v3sZz8zmZmZQeNSU1PNz3/+8zOu5XQ0tMH/vrrLy8tN27ZtzVtvveWM27Jli5FkCgsLjTHHvoRhYWHG7/c7bV588UXj9XpNZWWlMcaYhx56yFx44YVB877llltMRkaG837w4MEmKyvLeV9bW2sSEhLMjBkzzll/G7JgwQLjdrtNdXX1ac3bGLv6++Mf/9j8x3/8x0nn29rX78CBA83dd9992vM2JvT6W2/dunVGkvnyyy+NMcZs3rzZSDKffvqp02bJkiXG5XKZv//978YYY+bMmWM6derkrEtjjJk6darp06eP874ltlln0t+GzJo1yyQlJTnvQ3Ubfab97dmzp3n22WdP+plQXb9N6QdxCOlUdu7cKb/fr7S0NGdcVFSUUlNTVVhY2OBnFi5cqK+//rrBuwKPHDlSsbGxuvLKK7Vw4cKgaYWFhUHLkaSMjAxnOWdSS3MpKipSdXV1UC19+/ZVjx49nFoKCwvVv3//oJsLZmRkKBAIaNOmTU6bU/W5qqpKRUVFQW3CwsKUlpZ2zvv8XRUVFfJ6vWrTJvh+j1lZWerSpYsGDx6sV155JeiR77b197XXXlOXLl100UUXKScnR99++60zrTWv36KiIhUXF2vcuHEnTLNx/VZUVMjlcjnPfyssLFR0dLQuvfRSp01aWprCwsK0du1ap83QoUPldruD+lJSUqJvvvnGaROK26zv9vdkbWJiYk4Yb+M2+mT9nTlzpjp37qxBgwZp9uzZQYd3bV6/pytk78R7rvj9fkk64Q6/cXFxzrTvevnll5WRkaHu3bs74zp06KCnn35aV1xxhcLCwvSXv/xFN954o959912NHDnSWdaplnMmtTQXv98vt9t9whfmu/U2VGv9tFO1CQQCOnLkiL755hvV1tY22Gbr1q1N2aVG+cc//qEnn3xS9957b9D46dOn65prrlFkZKQ++OAD/eIXv9ChQ4f0wAMPSLKrv7fffrt69uyphIQEbdiwQVOnTlVJSYnefvttSa17/b788svq16+fLr/88qDxNq7fo0ePaurUqbrtttucB/f5/X7FxsYGtWvTpo1iYmKC1l1SUtIJddZP69SpU0husxrq73dt375dzz//vJ566ilnnK3b6JP194EHHtAll1yimJgYffLJJ8rJydG+ffv0zDPPOLXauH4b4wcfYBprz549WrZsmRYsWBA0vkuXLsrOznbeX3bZZdq7d69mz57tfDlgh0AgoMzMTCUnJ+vxxx8Pmvboo486rwcNGqTDhw9r9uzZzg+cTY4PZ/3791e3bt00fPhw7dixQ+eff34LVta8jhw5ovnz5wety3q2rd/q6mr97Gc/kzFGL774YkuX0+xOp79///vfNWLECP3bv/2bxo8f74y3cRt9qv4e35cBAwbI7Xbr5z//uWbMmNHqHyFQ7wd/CCk+Pl6STri6pqyszJl2vHnz5qlz586n9R8+NTVV27dvD1rWqZbT2FqaU3x8vKqqqlReXn7SWk7Wn/ppp2rj9XoVERGhLl26KDw8PCT6LEkHDx7UiBEj1LFjR73zzjtq27btKdunpqZqz549qqyslGRff4+XmpoqSc7/2da4fiXpf/7nf/Ttt99qzJgx39s2lNdv/Y/bl19+qfz8/KC/zuPj47V///6g9jU1NTpw4ECTfH9bYpt1qv7W27t3r4YNG6bLL79cL7300vfOM5S30afT3+OlpqaqpqZGu3btcmq1af2eiR98gElKSlJ8fLwKCgqccYFAQGvXrpXP5wtqa4zRvHnzNGbMmO/9YZOk4uJidevWzXnv8/mCliNJ+fn5znIaU0tzS0lJUdu2bYNqKSkpUWlpqVOLz+fTxo0bgzaU9V+05ORkp82p+ux2u5WSkhLUpq6uTgUFBee8z4FAQOnp6XK73Vq4cKHatWv3vZ8pLi5Wp06dnL94bOrvd9Vfelz/f7a1rd96L7/8skaOHKmuXbt+b9tQXb/1P27btm3T8uXL1blz56DpPp9P5eXlKioqcsatWLFCdXV1TlD1+XxavXq1qqurg/rSp08fderU6bT6e662Wd/XX+nYnperr75aKSkpmjdvnsLCvv/nLVS30afT3+8qLi5WWFiYc+jQpvV7xlryDOJz5eDBg2b9+vVm/fr1RpJ55plnzPr1650zumfOnGmio6PNe++9ZzZs2GBuuOGGBi8TW758uZFktmzZcsIycnNzzfz5882WLVvMli1bzK9//WsTFhZmXnnlFafNxx9/bNq0aWOeeuops2XLFvPYY481eMna6dRytn3++uuvzfr1601eXp6RZN544w2zfv16s2/fPmceEyZMMD169DArVqwwn332mfH5fMbn8znT6y+zTU9PN8XFxWbp0qWma9euDV5mO2XKFLNlyxbzwgsvNHjZqcfjMbm5uWbz5s3m3nvvNdHR0UFXvzR3fysqKkxqaqrp37+/2b59e9CliTU1NcYYYxYuXGj++Mc/mo0bN5pt27aZOXPmmMjISDNt2jTr+rt9+3Yzffp089lnn5mdO3ea9957z5x33nlm6NChrXL91tu2bZtxuVxmyZIlJyzDlvVbVVVlRo4cabp3726Ki4uD/q8ef8XJiBEjzKBBg8zatWvNRx99ZH70ox8FXUZdXl5u4uLizB133GG++OIL88Ybb5jIyMgTLrM9F9uss+3vnj17TO/evc3w4cPNnj17gtrUC6Vt9Nn295NPPjHPPvusKS4uNjt27DB//vOfTdeuXc2YMWNCcv02lx9EgPnwww+NpBOGsWPHGmOOXSr26KOPmri4OOPxeMzw4cNNSUnJCfO57bbbgu6Dcrzc3FzTr18/ExkZabxerxk8eHDQJcj1FixYYC644ALjdrvNhRdeaPLy8oKmn24tZ9vnefPmNTj9sccec+Zx5MgR84tf/MJ06tTJREZGmn/913894Qdh165d5tprrzURERGmS5cuZvLkyUGXHdfXMnDgQON2u815551n5s2bd0K9zz//vOnRo4dxu91m8ODBZs2aNee0vyf7vCSzc+dOY8yxy1AHDhxoOnToYNq3b28uvvhiM3fuXFNbW2tdf0tLS83QoUNNTEyM8Xg8pnfv3mbKlClB94ExpvWs33o5OTkmMTHxhHVmjD3rt/5y4IaGDz/80JnH119/bW677TbToUMH4/V6zV133RV0zxtjjPn888/NlVdeaTwej/mXf/kXM3PmzBNqORfbrLPt78nW//F/o4fSNvps+1tUVGRSU1NNVFSUadeunenXr5/5zW9+Y44ePRq0nFBZv83FZcxx1wgCAABY4Ad/DgwAALAPAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1vn/tYZeZT5wzS4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.axvline(aDP, ls=\"--\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "551454fb-d57c-466f-8b31-39ccb2a728de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 187 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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iMymRVh5bvJuLn1/NujnjkeW2dc7V6fW4Guvb7do6mkhYhLOCqqrYnR4kTULRNFwe1fuleB/dypHnuzeU0LVBoVtKCAFBJlz5tVSi8nZJLhWF27k/ZR/1RsiPWgpAVNRUXI4yHCs3snXfywDsizOwZKDMNRu/ZWanV0k/4P2ldhhlDrwXjaRIqMB3vVJQf/FHoigynvfH3IBxY0XztlevHciE7tGn75slCL8xmqZRsLeKz5/eCkBn+gDww5jzMDprGLbpEbpfMJUxf/nL4R28E470vAxWzIN+MyF1PEgSVuDJjPbrJ/JrDQ0HCA8fTVjY8OZtNfYtdO/+FLt2/ZmNmy4HTQUgOfYJ6oKMbFuWT2hsAJffN5CXMvPoFB7Agxd4V2B+d10uu4vsqJqG3MZxyDqDAY/b034X18FEwiI00zQN3G60pi+Hw0Wl0Ypb0XApKm6PhktRcB5OFn6RMFStLCZEpye1WxiKR0VVNBS3iqJoqB4VxeN9rrhVVEVlS1U964M0dEMjcakaLk3FpWq4Nc37ummbW9VwaRruogaMWyt9vpbpGEjZX4Oz6fW/aWS++QrviwPeh/yoCABKS5d4N/QFvB306Vro5jNdCHaTDYdRx7KwISyJGEWA0siw6J/pXJjPoYjg5mRl5OxbWf3ci6TkH6T7ip/JCvTWN0/pGcPYjCgOVB+g0dNIj4gep/AvJAjCL2maRvbG9Xz+5KNIchCm4N8fVUY1mgi76AJCLr/8yMbDs6NFdoUr3jhN0XpFhI8lv+AddLKpub9K165ziY25BL3eRnHxZwB07nwHERFjSfvVXHF9q0N4c81Bnvl+PymRVvIqG9DLEvJJzEpb37iHisJCvnv7PjRNBU1D09SmIc4SaIePKVFVmI/Su+MnrjsekbC0I03VvB/MHhWnR6FBA7eioqgaHlXFo2p4FK3p8ZevVcobi5HMBVhNEh7Ng6IqeFQPiqbgVt3e15oHY14ZPbZVEW+KaU4sNLfrSJKxYweuAzlYR49qSj48vyj3iy/P0dvxHJ1pv9j7Er5MGXHCa/9rtYVioCKrBlkvodPL6HQyOoOMrGt63bRd1ssE5TQw0CpTONqAQZIwyhJGScLQ9GiUZYyyhKFp254AOx9vrWRM7xiu6ROPUS9j0skY9DJGnYxBJ2Nsej7zyVUUulXskoask9iGmx84umOZcUc8y3ukcJ60CputJ9biSBL7F1ORPpT/K6/EaZQJ1MEC/T/4pFc/MgwK1arGWw9cxrOPP0hMmXeBsIRuPfj5rX8zfmcZBlVlVPabbO43g/OvnE75kAau/WYW28u2Y9aZWXzpYqIColr9Hrpc5RzIeY6wsOFERkw846bFFoQzyaeZqznw6Lzm15pqx2Iz0GdcInvXF1NV3ED/SUkMmNIJo3lqu5/f5VHJLKjBbJDpHhvk8+9rauqfASgpXYIsG0hLu5+E+FkAREaMJzJiPACeqiqcWVkYk5ORDEeGM1/UJ47dRXZeWJ6FS1HpnRDMc9f0Q5YlPKoHCQmd7FtHXMW0Gbcjkl1LM5sniZMkQAJJ0o5MHCdpGKwauuBcn47bUUTC4gOXw8O3r+zEUedqSkiOJCaHX6tuFVVtuWTUIquTAwbVp3NYEt5Eb9tz1HadpEMv65sfn5tXidkF9oQEJIPhqC/XgRwA7+uAgF+9b0TS61vdTzK03I7BwM77HmRwoMKsW4Zi1HuTApO+KTFoSg4OP3/ygZ+QJIn/mzfKp+t9+p3tGLdU8mL3ZJ/K7wmw8DF76JEayqQeMcctG2jWU+V0sbzBw4DJyeykEde6Q/w74yP+GrMFrBHk97qWK9buAuCKQS/RLbBlh7XlNC0wNrYvw9bt4vIgK891S6JRUenyv3XcPu3PXOOpxrz5I6o9O7l6TQlbUgwUhyRzUb6VQT+/hjuoEzMqHm4+pkNxkPWvf6BZk/CUl+MpKyP24YcxJiWhqm727X+MkpIvKCh4l/Dw0aR0vpugoF4+fX8E4beiNnc7ttdHMlXR8TzeZpWEbj255N65mALMAAyY0qlDY9hfUsv1b2ygoNo7qmhgciiv3zCIIPPx50nZvfpHVn/wNrUVFSR0G8X5N99OSExsizKaplH6+L+ofOcdUFX0UVHEP/00Af37ASArDuZor3F/8CdoshG514002vrxwE8PsCRnCRISF6ZeyJwhczDpjj8Mud/kS0ge/BNDh3x9wmteu24SEeHhJyzXkUTC4oPaCge5Oyvo3CeCwFBzi5qCw891em9tgk4vo0oaP7y+m66BFh6e1QOdLGHQyd5HWUavk9DLEnqd3PQoMe2j1wmRB/LFVc95ExRZh17SH5W1737YWyWX9v3SVmM9cOllOPfvJ/H550/pmu998E0uURSCZJUhKSf+IZUMMjgUn4+vN+pQPL6vCRpk9P6o1rtOfI7YJBuaqnHTVf0xWfUMlST+ccnhCZ0mAZAAFI/t69MCXyZZptqjULuxhOwl2XzV4OGQKrPP5aKmu0pp7QFkDaKq3VhcOWzT2+gJfGB/ght63MAbO49UOYd+uIxfNmxlT5xE2QsBqHoPilJPctItBAX3JSvrCTZsvISIiPGkpd6L1Zrm8/dKEM5FjtpKHn/qWYy4+D/ApFOomdaLfhP/yvSYsNMay9wvd2Ix6vh89nlUNri44Y0NXPf6z3z6p/OOuU/JgSyWPPckXQYPo/+Ui9jw5Se8c/+dzH7tf8i6IzUidT/8QOVbbxF1960EJBgpe+U9Ds24hvStW5HNZvh+Lmx5F2nYn5CcdbD8MV6o2sJ39dnc1u82VE3lmc3P4FAcPD7y8eNehyTpUVUxrPmcUlXsXSEzbWAUXQcd/+7+sO9e38XA5FBGdon0qbxBJ6OT9AQaA49b7sDYLpj35XGslkRJp2taBOLUjPr2HUKdddhTfVvKXNLLaIrvnbcMRhmP4nucwUbvnUud68TnMBl01Dk9mANb3u1UVVVRUlJCWGgYpq0N1K0rAo+KpWcEIZekIpta/3W4t3MMby/Pwr65gDzVQ5BLo79JR7TmYP3e7mQ0qqjsIEALoMAkkV7onQzqs2ENjHVUMKXTFArqCqhyVlESkkN0NegiIlDKvb3+DXUBxPS+krDwUQQHeTsLRkZMoKRkMVnZT7Ju/SQcjaPp1+9R4uLifP6eCcK5ovrDP1G76zuQZuHCyMPcyZ3XXsK9SX0IPIV5SE7WwfIGJvWIoU9iSPO2LbnVx90nb+d29AYjF9x1H7Kso7Iwn21Lv6Y4ez9xXTOayzn37SMwRUdY5RNIRbUk9YXaCDPuglxMqV0haxn0mwXjH/Lu8PMCNpdsYkL3y7mx540APLP5GRYfWHzChEWW9Gia7zea/iYSljYwHuMD7VjakjdoGr51mjpBmfZKWAwWE/u6juPiOX/yqbzOIKOcIAHRNA1N8z4aZBmPAntrG30aiqdpGhpQVu1kX0mt9zhoqKr38fAlaxrsK6klPLBlVeiaNWtYunSpt2Mx0MOTwPnnjUe26LF/cxC1wU3EDT1pzdTIELqpVqrDFDYedBBY/wFJ0VeTGJDKmrIfqLAOZllfHW7NO5Lo51SFLV2K0UICyavNwygbsZlshFvC2XdBIOGFVoIs4XiUWkody7F17kfnzre3OGdx8U6ys7dRW2fHaISKComXX36Z9PR0Ro4cSUJCQmuhCsK5RdOo+GcGOx0RXOt+hr/oP2R22BakOzaB3ui3sCb1iOHddYdwKyqVDd55TJ69pt9x94nslILH5WTNR++RkNGDvJ2ZAEQktWwWtw4bRnDO/+GsjaEx4VYal75PXJ981MxnUTs/gxzVDfYugS7ng9PbabeHKYLFeStYtG8RKt5uCMNih53wOiRJj6aJGpZzSmhMAMBRd+zHo0GbEgcVrX0WxtTr2yVhUfUGJJfvEwrpjDIBTnj1npWoqoamHElODj/SSlhjf97TYljw8ZiMMj9uKODHDQUnLBsXcqRPit1uZ+nSpQwZMoTzzjuPdW8uZXXFdrrE1tC3b1/s3xzEsbfquMcLjwnEsKOSvkFuampldJIBl6phCvodnoYfceFCH3QFlQElBBevpHd2PJvTDzF/wnwCDAFHDjTuyNOami1kbfoemyn0qPNt2/4Eev1ajEZwOXsyc+bLZGZmsmrVKl599VU6derEhAkTROIinLM0t8rq/8Qzwt3AKF0xuGFMn65IE+f5NVkBuH9KBmaDzDc7ijEbdDx+aS8u6nP82s+knn0YdvkM1n/6Ees//YjA8AiufvgJjOaW/ecsPbtBgEp1jonKbz/F1H8o8DHy1nd47rMShp4/ioHWQ0jvX+ndIW0Cd1z8PMUb/snctXMBmJA0gUdHPHrC6/AmLGJYs0Crn8/H5NTvxan28emY0nEOfLiGpTEzk6ZMoWmo2uGEoem1ooCqtv6oqAQ4Gyisb+TVVQdQNQ1VA0X1Hueo55rG+6UV9A8ycvv4ZGSdhCRLyLLU1OPc+yjJ3oW0JNkbSrFR47MU32eQrEhJIEz19vuRJG8HdkmSmh5BQmqugEoKP5Ik1NXVoWkaGRkZ2Gw20jt3ZXXFdnat205XnXflUdl2/GQ0cHgczgM1DN6nQvy1NChOVtXJSMgorh24DQ0ciN5Lj/IJOFjZvF+Dp6FlwvILh6tiExJmtfYuTmcs06b+1Lylb9++9O7dm927d7NixQpeffVV4uPjGThwID179sRgaN+F0QTBXwpLa7j0P6uRU5J5Usvhn/EpHPzdNGCav0MDwKiXuXdyBvdOzjhx4SaSJDH8ihkMvOASGmpqCIqKQm5tNI/eBOlTiTT9ROSVV1G2cQmqCvv7PEafLlZWfrEIZtzBoMv7gc4AIUnYgOfGPYfdZUdCwma0+RTT2nc7U1Uwj71fLGwexux9BGj5XFVup9v4Irp08fmS251IWHzR9CHYpooLyffy725dBkC5uq1tcbVCtlhA0zh4xZWndJwoYGNwJ974fj/exToldLKELHl/8XTSkeeyDIFhZkI6h9F/km+jfoBj9sM5ppDj9+85lujoaCIiIvj4449JT08n50AOFp2JIQcTqDy4B2OSjfBZx49GNuqIuKEHnrJG1n24kI0/LUIydcEQMI2wmM5UFu8nviAXl3sxmgQ7U2uZljKNCEvEMY8pSU1/rLTWRpLJtJbyyrJMjx496NatG/v27WPDhg18/vnnfP3116SmppKSkkJSUhKREZHNCaIgnA1UVeVfL7zJtuwS1pl7ezceeJDP4ut46YITN2+cLcolPUWWIDJUsB5rAuzpC3B//w+kAytwNjawr9udZFx6G+nAhi8WsfL9Nxl0sXdemaK6InLsOaQGpxJtbdsElYHBEVQV1BOb7kKSm24Am4c3e1dpPvx3fu/KSCTHgJO/8HYgEhYfSE3thFve/op9gd7x6ZLkvauX5aa7e7npjl8GWQK9Fk1uuZ1XVx1oUQsgS0dqB5Ak9lZv5fOSBwAYF3cRC/ctREJClmRkSUYn6ZoSBB2yJFPnriOsUcG+9BejhH6RGUmBVuTAQJLfeZvmIJGaxtUfCVqSZdDpmh+RZCRdy20ZVit/Pgc+8HQ6Hddeey3Lly+noKCA2LhYxswYQ1hACCgquiDfViCVJAlDVAAjb7+OzucPpDgri30bdVQVX4g5JBu5bjMeSyllGTaGTbiI63tdf4LjeROW/Vnz0OkC0DQPmuahpiYfgyEblzOEV199talZTUNV1aOeq6o32XG5XOzevZvdu3c3H3+WbiyhKVHoowKQJNAFm5BMOozxgejD277uiCC0N9XpZsvHX7KzJotxucPoqdPjCHZSWJWLUXUxdtJ4/jKl2ykt8Hem0DSNB7MKeDXf29neppN5oXsyEyOCW5RTNI2Hcu28FTgLT89ZJCVXcvlPX6Kt/hF7aQkAgy/xToI5f9t8Xtz6IhoaOknH3QPu5roe1/kcU1L3WIoP5DDtppnNMVYVN+Coc6PTy0R3PlIDXrpvHXq9bzU3HUUkLD4IrN1MonEP9RWB1Fab0ZDRNAkNGVWT0DS5eZva9Bgg11Jjz+e/SxvQ8OYUalPHUQ53GNXAGP8u+qaKg+WFX7K88Eu04zQmXSqp9KlSKbj9jmOWMaakYO7m3xkJzzRBQUFcfPHF7Xa8hIweJGT0oN8UlZyt5dgrUolJuYK4tBCfjxEQ0JmI8HGoqhNVaUSS9MiyCbM5kdzcRiorzyM6KqKpFktuuvs58ry1bdXLDlJpcxAaHEJoQhxKQQPOAzVoHhXNeWQ0gDHJRkD/KAL6RyMbz/4PA+Hss/XDjwjfHE20FImm994YJbqC+L5RpZ/Fzh8vGkrG8B7nTC3h8spaXs0v5++pcYwIDeSve/O5NjOHnef1JNx45KP4g6JKXi8o5/9SYkmyGLljFzwzaRbys39Hp9PTb8qFnHfVLA5UH+CFrS9wc6+bmd5lOq/veJ0nNz5Jr4he9I/u71NMheUHKbNu5qsPvX8bqorrKTvQiMkZgUdfR1QPHeZAI3q9TLmzghhP683bp4tIWHxgjO3CRWEz4HefQerYE+9QthdeGMyNN34LSUNPUHgqNc4agk0ts2xVU1E0BU3TUDQFVVO9265SMDe40Uvef7oWv8xNz2WLuHs+XXQ6mbQBrc9ceyJ6vY0+fV5p9b0hg9t+PE3VKPj2J0LHdcHayvB7TdHQnB4cWdU0bC6l+ots7MtyCbkglYA+vg2/F4RTpWka22/6PaY1a6jrew22TmMplcq5M+1xFk3+gP/GXthiXpJzxYFG72KoNyZEYJJl+gcFsLW2gb31Dob/YjqLbbUNpFvN3J7sbd55JLuQfAfMfvV99EYjBpN3cryCOu/gg4tSLyLRlsjohNF8vO9jVheu9jlh2ZO3Cbehho27j/S74xdjAKpKgJKmFyYoaTAAvU/2W3DKRMLii8M90n3N9JubaHwr/+tkBWhuEmqV2bcwhN+YwzMtN426UmqcyAEGNFVFMupQqh04s2vAIBN6RVc0p0LN1zlU/m8Pkk7C0vPY/W0EoT0U7/mZV1Z9xOVr1gBQefBrlvWzMW36FFal3dh6J9RzxJgwGw8DV2/LZkhwIF+WVZNuNTMspOVq0ENDAnm7sIK/ZxWQZDZS5vKQajFhDrS1uEHtF9WPMHMYt/9wOyMTRvJD7g/EWGO4udfNPsc0YvR5fP311zz44IPIskxdXR2LFi0iJ8c7Y7rVauX222/HaDTy/PPPExYvaljOfIc7R6q+TrBz6sOKhXOHUl1N9aJFuEtKCBg0CNuECR1Tzd2UKGtuheqvc6j7Mf+YRfVRFiL/0IewGRlUvLmTHxfvY7jNQFDy0cmzIJwqj8fDwi+/4ppt1/Eg8NqYCxm+YhPd7ryT86652t/hnRZpAWbe7Z3CEzlFfFBUybCQQB5Oizvqb8H0qBAONjqZn1dKnUdlbFgQ/81IPKpcoDGQNya9wTObn2Fl/kq6h3fnrv53Ydb7fker0+ma+8UpioLFYmHWrFnNI0sPn1PTtJNaXLG9iYTFF3LTt8nnhKXJGfAPLPiXUlNDzuVX4CkvxxAdTdXb7xB2/fVE339fu59Lc3s74FZ/lg0yWIfGIukkb2dbsw7JoMPcNRS11kXZq5mUv76DyJt7Ef677ly/ajscyCEztgeRRjE8Wmhfl3++lHVhnfi28z95NucxsqJCGbD4K8JSU/0d2mk1OszG6LDjd1yVJIl7OsVwV3I0qgb648xTlRKSwjPjnjnpeA5PhfDII480bwu01zLp22/RKy0/78YDVVdcDpMmnfT5TpVIWHxxuJrS1ymM29gkJJy77EuW4C4pIfXrJRgTEsi//XYq33yT0FmzMCbEd9h5Y/46CH1o63daOquBiBt7UvZyJtO/3Mq+IB0YvT+ro3/ew99T47kyJvSc6ewo+E9uo5PB63ZDmHeBvx3Jw1Av3sm8kKMnSxRakpumjjgej8fD9u3bKS8vJzo6mp49e6Jrpf+Poijs2rWLsrIyYmJiyMjIQJZlMjIyuPTSS1F+kZxIe/agX7IE9xVXoIWGNG83fLSQRIN/J+wTCYsvmmtYfJ0RsClhEX/wzwqNO8qpXZmP2uChJtHFV0lrMJnNXJx2MYm2xFM7+OGfgcPNNe0wC/GxyAEGEh4f6VNZY1wgkTf1ZNveLAY7ZYaZLEztFcezJRXcuSeXtwrLuTs5mvHhQWdEVbBwdqjOWolp71dQV0bnqDtbvBdl1PP94AxCDOJjpz2oqsq7777LoUOHCAkJYc2aNezevZurrrqqxc2Gqqq8//77ZGdnY7PZqK2tpUePHlx++eUYjUZ6927ZibZRljkIdJ01C3N61+bt2ct+wOjnySmPNW2NTx5//HEkSeKuu+5q3vbyyy8zZswYgoKCvMMsq6tPeJy5c+c2D888/JWR4fsMgh3ucOdXX5uEOvBDSWg/mqbhyK6m4t3d2FXY6KqiYr2TIQv7U/t2FPe+8hhZVVmndI6gqVMxxMZy4IILyTp/InXfLyPsphsxJsSjqRoN28uo/jKb2p8KUB2nd4psY6INzSAzqU8cd4/pSnp4IC90T+bjvqlIwO8ycxi5fg+Ly6rxqG37mdY0DYf77FlUTTh5iqbx96wCYpZvhQ+uwbJhAcbdnzW/Pz7Ewnu9U9g6vIdIVtrRoUOHOHjwIDNnzuTOO+9kypQp7Nmzh+3bt7col5ubS3Z2NjNmzODPf/4zEydOZOfOnezcubP1A+ua/o3asJjt6XLSPz0bNmxgwYIFR2VnDQ0NTJ48mcmTJzNnzhyfj9ejRw++//77I4Hpz6Af7DbXsBwm7kzPRCvzV/LvDf8mtzaXOdV/IE7uSebOSjRN5hAawTro2T2NoMwI7nhrDkvuWnjS59IFBdH544VUf/IJnuISAgYPInCsd2h89edZ1K8vRh9hwVPtoH59EVG39UM2nb6REmori26OCLXxVf9APi+t5rX8cm7acZAESc+0dbVMGpVE/z5RmK1H7rRUVeNAeR2fbingheXZgHfqcp0kkTl3InrdKd0XCWcoj6qxtrqOh7IK2F3vAGDkoLfJXHspOlS+2/R7IkJiiLvx8yPN6kK7OVxbe7gJSJa9v2eHJ5M87HBzj9nsbSI2mbwTZbpOsFac5v7VoogS+HtAyUllBXV1dcycOZNXXnmFRx9tucDS4dqWFStWtC0QvZ6YmKPnjjgTOB1Ont89kqSX3iIy5ktAQkNqWp9HO/J4eJ0etwNjTWeGu92I7otnloK6Au5afheDYwYzo9sMin/YR02lh5T0IEr32ellg7V2sHq8v/TT9tx6yufUBQURfv31LbYp9W7q1xcTPC0F28h43KUNlPxnE1UL9xI+q/spn9NXnqY/eoqq0KA4afS4+aH0AD+WFSNJMia3RiBWKDMTdbCRLQf3soW9hFTvw648y4sXeP9Y1u75B4FG75DHlEgrNQ1uKupd1DsVggNEwnKqNI8HTVWprqtj9+7d7Nu3j21F2zgYeJB9YdX8dfizjI5Oo06FXoGWDu9/pB1cQ0KO99873nTkr1y1PoilcZM5v/Abel/+NHQa0aFx/JYlJyeTmJjIO++8Q3h4OOXl5aSlpdG3b98W5Tp16kRcXBxvvfUW0dHRFBcX07lzZ/r1a3116ap33gHAlZ+Ppc+R9e0kSerQJm1fnFTCMnv2bKZNm8aECROOSlhO1v79+4mLi8NsNjNs2DDmzZtHUlJSq2WdTidOp7P5td1ub5cYjkU2e8fJ51Zo1DV4Z9GR0I4s7He4YNMCfJUNMh41gYxGI21b2UHoaFtKt+BW3Twx+gmCjEF85PyIsu0alkN1jA82UKt6AI2D+0oxYiZ1bEcP8/XfHwBVU9GAOfvymbP3IEiH/xxIoHknktMrlQRLFRAu82W/BsbstGBz2agO6cq4FRovXuDd443rhjO4czhWk/cYSzKL+NN7m6lzeQgOEGn7yXC7nJQcyKLqgw+RP/yYhVe1XB8shBD6VvZlY5duzDmkIh/czXi+Y7NnLHOVavqkxhEVZSEoqDOy3Pqfek1VkWQZzeXCkZfHlvJqCuKSqSquY1DjXhzhwfRI606QKQhVUajfko+85SOsBY/SZeBb7Ld2osDpvRM/PzwIm04mZODrEGxt9XxC+zm85MiWLVsoKytj6NCh9OnT56hkVafTcd1117Fp0ybKysro06cP/fv3P6qcq6gepdqBMX0w8DkNG/bgLqhv7ofnqbI3j0T0lzYnLB988AGbN29mw4YN7RbEkCFDePPNN0lPT6eoqIiHH36YkSNHsmPHDmy2o4eAzZs3j4cffrjdzn8iclOb3qRb76LnmAknLF+UtZf3//ZnZKvoCX+mSQ9NB+DpTU8zIWkCS4qWEBiVhlQ2nLJQM2XF3j++RsVE56EhTLqi9buQU6WzGrAOjaVmcQ7164rw1DjRR1kIvSK9Q87XGrfqxuDYg+wp45L4DAL0evSSRM/gCC6K64FBlprXsgJ4M/JdXtpv4NqmmuQas54LY+4jPSKZQLOBXUV2Su1ONudW8dpP3omnDLqTv9P/uqyatwoqmNc1gc4Bvq33dDYrrXWwdn0B3bdUYKlwUuksYmnh20zdlo3SVN0fpNMz9oJpFBcXs379epxWJx6z92fGgJtOHGCpYQo/ZO4hzDSbvHzIru7MS9v/QP/kRH4nLSGRen7OcmL4eTc9cgtbxBAKPDzqdoZH7uJ+ZRqF3W6Gjd73rreP4KqCGcAgrGZYtfE6Ykb/2LzvO71TTse3SfgFg8HA4MEnnhbbZDIxfPjwY77vyK6m/JVMANRGA8gGqj949ahyit2/Nx9tSljy8vK48847Wbp0aXN7WHuYMmVK8/PevXszZMgQkpOT+eijj7jpppuOKj9nzhzuueee5td2u53ExFMczXEcktxypMeJqB5vm+HhREc4c3QJ7cLfhvyN/2z6Dwv3LSTMHMY/fn8Dodmp5G0vJrk8j9C89URG6Ejof/ORf/sOEHJRKqbUYFyHatGFmLAOjD6t/VeMspGQ0seYM3gOM7qdf9yy5Y3lLNy3kBL9LSyw1pFRnc8rY/5G9XIbYAfWNpeND7Fw3bBk/jA6lWCjRHl5OREREaiq2tzOfiKKpnHDjoMADFu/m63DexBjOjdqahSlEVk2oWkqGytLeDK/kZ8/2Nv8/q2YmInJO1lXU38EQ88eXPXBh8T8/SFCm6ryR48ezWc5n0Gpd7+LWMRavE0wFveR/gmLD5xPlSOA9KzXGGP4CABXfSRLQjPoUiBjVFreNVdYgsnVokhDxy/TmT3hP0PBDEBHvuMLEswXsX79VbyUcDW/G/XbmPztrOWqh59fgZIdEJEOQ24B85Ha40a7i3K3SsyoeCLHDUF9aCx43L9oAtIoW7CdgL4d9znrizZ9om7atInS0lL69z+yToGiKKxcuZLnn38ep9PZ6hjwtgoJCaFr165kZbU+QsNkMjV3HDodpKZRQr6236nq4YRFtN23RVleLbvXFKF4VNL6RZHYPaxDznN1xtVcknYJZY1lxFhjMMgGtASVwGdux1NRTvDUqdRv2MCh664n9eslGDqob5UkSwT0iiSgl3/W8ZEkCb2sP/YSEE00TePhNQ9T56nmjT/EECglY9Dp0csSsiSh10nNHXeDLQYibUd+NxctWkRmZiaXXnopn3zyCb169eL8888nKCjoWKcDQCdJ/LlTNE8d9DbBjvp5N98PTCfJcnbXtFz1+DgaTTKTTHGsTe/O9/JkoOVqG6FIWM6L5uDelVx24d3Uz74Ddbv37teQkNBcLiAggBk9ZjCjx+EtffmhpIIf92UzdexoHI5wzObvmNUvl21Le5IslTbvW+/xzqexu1MiA5I6YwmwEjn7T5hSUlivamzPr6ZPQghr817iq30vEu7cyKAQG5bUABo/bcA2vhOMKiC5Jo/HzcEQFNeB3zXhlKgqvHsZFG6BuP6wZwns+hxu/gH0RrZ+n8vaT7JRVQ3p61yGyDIDLjy6tkwf7P9ZsNuUsIwfP57MzMwW22644QYyMjK477772iVZAW+n3uzsbH73u9+1y/FO1S+nJ/aFqGFpu6LsGj77z2aswSb0Jh27VhUy7toMug3vmD+EZr25xRwr7txcnHv2kLhgPoGjR6PU1bFv4CDyZ99G50Ufd0gMZwK9pGd72XashiN9DjQ0NFUlOn8zBncDZfWlhBSs4tkul9KnMhO6xoPNt3WHDs+k+cknnwCQmZlJZmYmAwYMYOrUqS3/Zmx8A6e9lIVrssnyxDFg3xYWzrmfq4plpHo956/fw4LenRkTdvxk50yjaRoVK3ZRduvlzAXWdJP4YWQ0290DoCn/+uZvY4mWdciKRqAsows0cvGFf0NTVTzLvgeDAdliQddKE/kvjYsOZ1x0eNOrIcBNDAGuGA97i0exuXAPafoKYrRQxtQ0EN05jYRf1U7rZIl+Sd7m7POSR3Be8q86zg75xfMosSr8Ga9oK+SuhVmLIG0C5KyEty6EFfOw972X1Yuy6D0mgYz+kWx4YRvrFh/EnFNNVICB0Mu7og9p+iE9Awa9tukT1Waz0bNnzxbbrFYr4eHhzduLi4spLi5urh3JzMzEZrORlJREWJj3jnn8+PFMnz6d2267DYC//OUvXHjhhSQnJ1NYWMjf//53dDod11xzzSlfYLuRJN+bhA7XsOjFUD5fZa7IJyQ6gCv/bxCyTuLFW5fzw9t7Oixh+TVdRASSyYR9yRKMnTpR1zTKLXj69NNyfn+JD4znywNf8uWBLwHo73BwfU0tpTodQ2vrmstNBihvatMedS+M+9txj6tpGtnZ2c2LqAHcfffdNDQ0sODrjdy+WubNfV/z5yk96Zv3DgFrnwS8n9+zDu/QFWre2crf7G80H+OZXQ280T+CVwenYejA5rr24PK4OPTCOixFLUdXVFtBkRTei/PQqXsvDJKE5Ri1sZIsY4hvnxmR02NsEDMIgCAg4fjFhXOFweJ9rPGu7kxtsfcxMBp7eSNo0H1EHOHxgSToJXKA3Px6gmVwl9QfSVjOAO1eBTB//vwWHWJHjRoFwBtvvMH1TUM7s7OzKS8vby6Tn5/PNddcQ0VFBZGRkYwYMYJ169YRGXnmLHkv4fuQLrVpwp1zeeXR9qYzyHhcCm6ngsF4+r9vusBAYh6eS/FDf6fm8y8ACJ0xg9CZM057LKfTxxd9jFt1I6kKui3voP/+H2AKQqr1NsV4/rAKKTIDSdZ5f56fTG91Tg2n08mKFSvYsWMHAQEBOBwOampqiIiIYNSoUYwZMwZZlgkODubNfd4P501lEjPe3sl602sEHCP3CA6oJqLhAOUebxX1hO2NLDZUkNhYz5s9OzM50v/V1L82d9EmPthSgqHLfXxd9CLgraX96qbpXPPHP3GTIZC7jUE+9+cRhFMS1Q36XANf3gFLHwJHNaRPhSF/INqlEhBs5JuXd9C5dwT7GxQMEmRIGiAd3YfPz3OiSpq/B1a3A7vdTnBwMDU1NSdsGz9Zh/7aCU9EN0xJfb01LZqG919Pa/pHbFrdUlOprSjn0O7djH7iGywhvlWd/9ZVFtbzyZOb8LhVZJ2E4lKZcmsvOvU6vd8/T2Uljt27MSYkYExOPq3n9gt7IXz3oPcxd423jXvWIu8ftYZKSBjYonjjY9F8EZPCkjjvlN2SJHHo0KEjq1H8ot5YQuLekfcyqceRxdIUReHfC95hfu6RmxEZlSzTrOZ1U/aOfpFOYRZMn95AlZTEz2Evkr3bg9Y0227ZqHDmx3qfX1dr4JGJ6RjNHdf8WmJ38MHPeWzfUMDQGoU+DXnsql5DhbMQWadDVRQsIWFInbpSqzPyYqW3U4khZB0RUd8hmWTO7zyRuIBYbup5E3r9udF5WDiLaBrsXwrF2yEyA9KngKzD4ShkX+YXZK/uSk2xhMVZR3qinfDUAGS9AXOXSCS9HlnWUb34ILb4dMIvad9mwLZ8fouExVdzj38np2ktk09ZAmXGInRdTzwMWvCqr3GStbEUxaPSuU8EoTFiLocO91gcuOu9zyf9E4bNPm7xxodD+Cghg33p473L0qNx8OBBauw1WK1WTCaTdwJFYJdjF33VvkyPnI7RaERVVfbv309VVRUhkTE4A6Lo3acvKeZa0gMbwVkHBnOrk4012F3k7a6kpqyBDV8d5JGrvM3L139vJ6S2lAPn/cjfpvyF5KD2STJVp0JjZjm5s8YAsKDnRXyWNooIJD7D24/kh6L3KXPkNe8jp/XAGJdEZWUlu52hrPF0JkAP6+4fw6rXF7B3zY9oGphtPRg582Z6j+3cLrEKwsk6dGgBWdlP+Fw+2nMlPSfOa9cY2vL5LXqF+kiTZLSJj8Ggm5CQQZa9o4eaOuRK/KJPUm0xPJWODrGWSltYg030Ge/fYXNngnp3PZtKNmHSmegf3R+D3IF35O566DoZLn4RrOHHL6tpGDWN7tH9uG7EY0e2H2O9xYnvT0RxKRQXF+NyudA0jaSkJMaMGXPMVWWPJSDISPqQGD587GfvsQ96sEcYuP26Ljz27lLWOlZwwacriC0382SPv9Nz4tSTanIpKipi9QtLCFdtdNFim7dPqCtmQno8A8IC0XaUo9a6ueCO+zB0DuSpZ55tXm8syOlEURS66stJ0NUwZPAgtn7+EXvXrsIcPJLYtGAObPycZa89jzX4PlL7R7U5RkE4FdtqG/i4uBJVgwkB3plse/V6EWtAGqrDw96ffqYoK5uAIBvpg/thCTCjqgrbC65HiSo/wdE7lkhYfCTJeiSdAfQ+dECSmv4Q+7pYoiA02VWxi1u/v5VKRyUAaSFpvDrxVcItJ0gmTsbhxc0yLjhxsgKoigsdIOktPh0+MiSS5OBkbjnvllMI8ghN1SjP83YEnlqgUvDN3/gAiDUqkOotUxTh4OVvnuIPL68gcsgUQqb3x9Ql5LhT1Vc6KlmWu4yd7zctBqeHUDWQkXddhHXwC9Sv2k5fc1/YW4tmbSTuwWEt9h8/4XyWLVtGZGQk1dXVjBs3DqvVSkxMDHFxcfzvwb8i61M578qr6DM+kaeu+hzVvZdvXt7B7Pnj2uV7Iwi++LGylhnbs4k1GZCReL3AzD0MZLA5Eas1laXvPc/2Zd8Q1SmVg5m7yNqSybX/ehaLLQibvQcmi3+XzxEJS0c4PK+FJhIWoW3+veHfRAVE8c6Ud6h2VjNzyUx+9/XvWHLpkvY/mce7YF3zKIITcLnqMAOS3rdJI616K/WHm5vagSRLjLs2g9UfZxGZZKO+LI3q4ixGTZ3Nx445qE2/dj/2K+d+88V4qqD89R0AbAj8gS7nDSc8MQmjOYDdxTv4255/EhoYTo49B1VTGW0aTYTT22eqTnbw8esfoKLSEGNklCoRUA1q/dELoI4cOZKRI73VTJqmHZUcxaR1oTj7WzYu/oqqQm+NiqxPpN/5rS89IggdZUFeKf1sAXzWrwuSBPErtvGUNIc/aB6cDfVs/+FbRv/uJgZeMJ3ainJe/tP1fPbvR7nmH0+gaSoS/h1IIhKWjnC4KlrUsAhtVNJQwvC44SQFJZGoeZvH8mrzTrDXSTq8+riPo9ncrlrMgOxjgmM1WGlwN5xkcK3rNjyueaj7avMgdqyoZvQ1F/DAjyVs3reGzIodnG8fdtR+eTu3c2DHxuapCQ7G1FPZvxp3vYcHhj5ArauW//JfZhTPICE0FmdVA3vqDjbv70rpTdy4VExJJ+gU2EpNzoirfkdFfjG5mYvZVACyIY1eE65n6PTUo8pqmkbDunU0btuGIT6BoEkTkYzGtnyLBOGYDLKEQ9VwaRryLzpdamhIsowsyzjr69A0DWeD92bD0jT3j6YpR27G/UQkLB3hcJOQqGER2mhq56m8kvkKNc4aapw1ADw99umOOZnWNCX7MRbG+zW3s7apuO8JS3ljx7V5G0wmPA4HisfNZSOu5/KRN3Bw22YaFnoTPEX1oJP1bOMn3KqT2a/9j5rSEjwuFwcqsliR9TfmpNzBhV2v4PyPzyfJk4Sz0UmZoRJ7o3dB1YyMDKZMmULwKczyabQEcMUDf6exro7acge28CAsttaTkNIn/k3lG2+gCw5Gqamh6v33SX7rTZG0CO3ijqRoLt+aRe/VO5AlMErwV/Uh0OZiNFsYfPHlrPvkQ7Yv+5bGWjvhCUlMvf0vvzjCWbhas3ACh+9YVf+ubCmcfW7tcys2o43vDn2HSWfiydFPMj5pfMecrLmGxceExeXtP6IzBvhU3mqwUu9pvyahXzPpDegqK3n2motRf9HBVpJkNO3o3z1HfT3RKWkAKMUmyIK/73qcf+z6F06dynjZ+30+vPp7eno6V1111XH7v7SFJTAQS2DgMd/3VFRQ+eabRN59N+G33Ezjli0cmjGTksf/RcxDD7ZLDMJv24BgKysGZ/BFaTWqBuMCqyndvpPK7PdRjOtI764QZBnAvgOFEJlIdL/ObCn4EBmN+vp9BAcPPPFJOpBIWHykahqF+7fiUj9H0yQ0SWqegkVrGh+kIaEigeKiG6Aobr+1+OU2OllSVoMkwbTIEBLM4g7tbKCTdVzX4zqu63Fdux2zvrqK1R+9S3HWPkJiYhl+xUwiEpOhsdpbYNVTULjVWyOoKt6aF03xJtxa02tVQV/pnb368c1P08VThnT4P6nl4+G1ibaVbTvpJqEtpVv4OudrdJKOi9MuJiMso8X71YsWYXrsn4xuaECzWNBmXIU6sJ93rhZNo76mmjUfvUtobBzhCckEhIQQEn2kw2BceBKX2gdzqHAfAAHBIcy4bgZFG4q8rwMCGDFiRLslK77QXC7QNPRRUd51npomzvSUlZ22GIRzX7LFxO3J0QA4y4spBQ5Uf3qkQCCE9PY+dRaD8xf7bqnMxp+LMYiExUclio2E/e/C/nd93mdzicKgDozpWDbU1HPl1uymVxr/yinm476p9A8S85qc7dZklfPMsv2U2B0M6RzO/VMyCLUeOxlVVYVF/3yI+uoq0gYNJW/XDj78+33c8PQCAg53iM3f4J22W9Z526gPP0q6puc6kGWsqpt1ZhMlliA8Fbu9aw41zcVy+FFtqtnQNA0VlUExbf8N+O7gd/z5xz+TaEvErbr5YM8HvDzx5eZjufLyKHrwIYIvvpjgiy+i8p13qXvtTdKmX4YpzVuD4mpsYM1H75LSfzDRKWl4XC62fPMlzoYGakpLyNq4jqBaO6Niu5PQrQdpg4eREjsILmpzuO3GEBuL7fzzKXrgAao/+ghnVhaGhARi5/3Tf0EJ5zQTAYxYW4F6wRMQ3QskHVvKtnPv1v8yKaQbM4b8FY/qQdHgwTVzGRzf26/xioTFR+OdTzI1zczNI1OQJA1Jw/vYVL8iSSBpGpLkvcOb+cYW7ggb7ZeE5T8Hi+liNfFpvzQ0DdJWZTJ1036Kx/b1QzRCe9lfUst1b/xMn4QQxneLZtHmfHIq6vnoD0d3Mj2sqrCAskM5XPZ//6BTn/401FTz0i2zWPLck1z+t0dgbo3P5zcDQ4Glp34px/XWrrcYET+CF8a/gKIp9H+nPzd+eyOZ13kXXnXuzwJVJfK22Rji41EdDuqWLaPmiy+JuuduAPRGE2ZrIJsWf9Z8XJ1ej9ESQGBoGD3HTKDnmPMJTziz5v2Jf+pJqhYupHHbNqzDhhI6axa64zQjCcKpMrk13EHdeDrvJz7P/hxVU6lVZOJ1UaRGHlnp0iWZ0ev8O0uzSFh8pBoC6NEtg/T0E89OqWka+Vo+/urB4lQ1gvU6LGKtknPKD3tKMehk3r95KEa9TGW9i0+3FLC7yE632NZHr1iCgpF1OrI2riciqRP7168GIG3g0NMZepvoJT2NnkY8qgePevQwYkuf3sgBARTcex9BkyZS89nnAITdcH1zGVmn45YX38TjdqE3GtEbjEin8fdBVTV2FdlRVI0ecUHoj7G44a9JRiNhM2fCzJkdHKEg4K1dBV7d9wHvl67h+h7Xe19nvspPtQe48RdFFU1BJ4lhzWcFWZJ8XazZ25YvgeqnDtU3J0Rw446DjPp5D6rmnYH3kaClLF8xC70+kKTEG0hKuuW0ts8Lpy4pLIAGl8Inm/MZ2CmMvcXeUTtxIccetRMQFMyYa3/P8rdeYdt3iwHoPWEyfSZOPS0xn4w/9PkDs5fNZsxHY1BUBYvewhuTj6zYrA8PJ2H+S5TMe5zSfz+JKSODTh99iD40tMVxDGYzBrNvc8a0p4o6Jze8uYHt+d7aq9RIK2/eMJjEMN86KwvCaWP0dhNYV5fL+KTx3Nn/TjRN49XMV9nQWNCiqEf1oPPzgr4iYfGRhLfjra9kSULx0zJNUyJD+LRfGp+UVAFwh3EhsXWfk9zpNhzOIrKyn0CnCyQhQdzFnU0m9ojh0v7x3P+Jt2nEatTxyrUDCbYcv5q23+QLSR04hNKcA4TExHo73J7BhscNZ9FFi/j+0PfoJB1TOk8hLjCuRRnr4MGkfPqJnyI8vmeW7Se/qpF3bxqCySBzxfy1jHtqBfsfO3OTROE3KtLbhbZLUCe+LlzLNznfNK8xEyzpWfbTP9lYsBq3plDtLEVf598O4CJh8ZHcxtoIWfI2DfnL0JBAhoZ4275/XPkZiYk30KnTHwEoKHiXvfseEgnLWUYnS/znyr7cNjaNYruDHnHBJ0xWDguKiCIo4uxZtyYlOIVbep94Sn+Povrc3HK65JTX0ychmBFdjqw07lbO+jVmhXNRU5+U25OncVBr5K8r/wqAQdOowcNd2f9rLprhctO3ooMmsfSRSFh85G3i8f2PjiRJqP5qE/qVwMAMios/xWbrjtPhHbYZF3e1n6MSTlZKZCApkWdnR8yqqirWrVtHbW0tycnJDBw4sE2LIAIs2reI/6z6hqJDI1Fd4aRGWXjisr4MSA7roKjbZmKPGB78bAd//mgbJoM3mbpjXJqfoxKEVjQtsxH8/lW8agmlWALQCHM2kv2ed30xSa+CJqEpEtGXNcIlfotWJCy+UjUosTvJq2poXpX58KMsSy1eS5K3huUMyVfolvFPMjP/xPbt3jvWmJjppHed69+ghN+c2tpaXnnlFWRZJiIigm+++YaCggIuvfRSn/ZXHQ62LH6LTza+RTH3kxrupMy0mIKqgVz2UiMbH5hARKAPi5N2sFlDknC6FT7ckIeiatw1oQu3jRUJi3AGssXARc9BQwUAMdA0pYEM770IQOTlo5H0ekreXY5L6uS3UEEkLD5zehRe+ymH137K8XmfPU2dIv0tIKATgwcvxuEoQKcLwGg8M+5Ehd+WnTt34nQ6ueeee7BarSxatIjt27czatQoIiIijruvu7CQQ9deR0B+PmNje7J6iI6bl71LTVIIHydmspdo/rc+l9vHdzlNV3NskiTx+5Ep/H5kir9DEYTjkyTof22rbyW80J382+9AsWUQ9ed7qN50aXMnXX8RCYuPQgOMxIVYGNQpFE07vKJC0/831aRov3j+5pqDJIad/hEKxyJJEhZLgr/DEH7DTCYTqqpSVVWFxWKhocE7C67Rh3Vyyl+aj+Z2U3vhCOJX7EbWVOb3u4zuzjzs9Z0AmNL9+EmPIAi+s40fT+Ttt1H2zLNs3PAFnXeXYO4rJo47K0gSjO4ayd3nd/Wp/KdbCjC0sW1eEM5lPXv2ZMOGDbz66qvo9Xo8Hg/nn38+QUHHXwEZwFNejjExkZh6I3WdAhhuyGGTM4EDhr4EO+oYH7iPn5fXkDZjxmm4EkE493kqHRA4GuPUeDJqy/CEfcrW+v3EnXjXDiMSFh+V2J2s2Fvqc8Iit7GTriCc6wwGAzfeeCN79uzBbreTnJxMfHy8T/sGX3wxBXfdhSE5mbCiEtK6V3CJK4uoH38maOQQPtOFIkkxJz6QIAgnpGWvovx/LjRVIjt6A3JQF7qG/5Xsnq/5NS6RsLRBW4YmtmWiOUH4rdDr9fTs2bPN+wVNngTPPkP1RwsJ9HjokpPDjpQUgi6eRL1eT3hIiM+ddwVBOA6PE89bt+JxvYSUPY+upXuwxxqRO73CpQen+zU0kbD4KMxqZFrvWJ/Ly7KEcqYMExKEc0DQxIkETZwIQKqmsWfPHnJzcwkODqZv376YTP4fISQIZz1VQXVWoalOPAFj0dlqsToGA2DybAP8l7SIhMVHbZ3EXjQJCULHkSSJbt260a2bPxe7F4RzkDGAA18Eo094h4ChN6PFDsWEhnPvEqQUt19DEwmLj6Q2zlyrk6QzZh4WQRAEQfCFpmmgaXjyfsZpa8RdXI8cGoAneweGjCv8GptIWHzkjHiel7JreOdQWFPfFLV5aLPmHdDc4rkjFPId1wG+ddIVBEEQBH/T3EdqUZy7vOuWqZXe18VKNEn+CKqJSFh8ZTmADATJGUgSSHin3JaQvKszNz02beGQbhmBwYf8GrIgCIIg+MpR58blUOiydSuvzl5KsD2H0Oq9yKqbQ0mTSe/X3a/xiYSlDa7rfh1/GfQXn8pO+2QaIVbx7RUEQRDODu88uBZXo8f7whBAZXgP+tx1Gd3Pi+PDx37G370cxCeqj0JNoYSYQ3wuL0uyX1drFgRBEIS2cDV68Mgw74owdIpGcIPK/Opi+KoYT28dMzweRvsxPpGw+EiSpDYlIJIkoWpqB0YkCIIgCO0rKCMEAEUnMUpvRgWQ4Quzm/Jgiz9DEwlLW2htqBCTkNpUXhAEQRD8afb8cdR7FOasyuS/GYlcExve/F7Ohr2YrAY/RkdTz1Gh3Um0rUZGEARBEPzNIHtnHZN/NfuYomnopLbOSNa+RA2Lj9qagEh+/ocVWnKqKt+U11Dm8jA8JJDugf6t2hQEQTgTGZo+u+7ck8sjW/cAoElQYQog1eOArgl+i00kLD6y19t5fuvzrNm5pnmbhIT3f1Lz68OJSpYzi562tq+ZIrS/eo/CJVuyyKxrxCRLOFWNeV0TuCE+wt+hCYIgnFEkSWLU3i1UWYOIiopEL+uQJCjK20//iBOvrN6RRMLio6S6JKpMVRSqhd55+psqW7RfTB93mIZGiCcEW43ND5EKv7awpIo99Q6+G9iV7lYLF2/Zz5x9+UyPCiHEIH4FBEEQDps7dy6HZ1uZlDaarl0TAXhr+1oSQtP9FxgiYfHZCNcIBvYeyJgxY3wq/9JLL5FsSu7YoASf1HoUjLJEJ4sJvSwRovf+2DuOsXaCpmkoSgM6XYBo2hME4Tfj190evv32R7799sfm1/UNmcCFpzmqI0TCIpzzpkeH8nxuKWN+3kOKxcSa6jpmxoYRYzq6x3tFxUr27H0IhyMPszmBjPR/EB7uz5kHBEEQTg9NczN02EdUVsSjN7gADZMpipCQgZSWLiY9faxf4zulUUKPP/44kiRx1113NW97+eWXGTNmDEFBQUiSRHV1tU/HeuGFF+jUqRNms5khQ4bw888/n0po7U7caZ+9EsxGvh7QlUkRwQQbdDzaJZ5/NVVz/pLLVcH2zD8RYEmme/enCLAks3XbjTQ0iCUWBEE4t2mqhqfQydju6xhWcQsZdUMJDy8gMHALHs8rhIUVopdcfo3xpGtYNmzYwIIFC+jdu3eL7Q0NDUyePJnJkyczZ84cn4714Ycfcs899zB//nyGDBnC008/zaRJk9i7dy9RUVEnG6IgNEsJMDHvBL3ba+t2o6qNpKfPJSCgMwGWJCo3rSY371Uy0h85TZEKgiCcXpqm8eK/H2F+zEIuqRiHJmlQraFVT0WVPNTGrEfVOZlWr9Hdj2NJTiphqaurY+bMmbzyyis8+uijLd47XNuyYsUKn4/3n//8h5tvvpkbbrgBgPnz57N48WJef/117r///pMJURDaLDAwA1k2sX//P4mJvZSioo8BSEy4wc+RCYIgdByPo57KuFWMCXBzSNtCpdOMpJNRZZVatY7GBgmHTibYrDLNj3GeVJPQ7NmzmTZtGhMmTDjlAFwuF5s2bWpxLFmWmTBhAmvXrm11H6fTid1ub/ElCKfKZIygZ8/nsNfuYMeO26it3UXvXvOxWlP8HZogCEKHcLtr2LjtUobGHWBqsJvfJRYQH1FAoamMXF0RVYZaHDpvU1CuXOjXWNtcw/LBBx+wefNmNmzY0C4BlJeXoygK0dHRLbZHR0ezZ8+eVveZN28eDz/8cLuc31dBQfvJydmCJG9BAiTp8Jfc6qPJtAMIO60xCqcuMmI8EeeNxe2uxmAIQZLEZNCCIJy7Cgs/xOEo5PFiM+UeiXtjHFwZ6uDnOgmQiAyIZUjcKL7K+pAuoV39GmubEpa8vDzuvPNOli5ditls7qiYTmjOnDncc889za/tdjuJiUd3omwvquokJXUpAB5P67U+v5bcCXSyDriqw+ISOoYkyRiNItkUBOHcpygNyLKJr69czuMbn8ZR/Q6gITfNN1bWUMRXWR8CsLPBv8vNtClh2bRpE6WlpfTv3795m6IorFy5kueffx6n04lOp2tTABEREeh0OkpKSlpsLykpISYmptV9TCYTJpOpTec5FbLsPVdM9MV07Tr3VyOGjjxXVRVN01BVla3briIsTHQYFo5N0zRqSorRGQzYwsWsu4IgnH4xMdPJzXuDn3+ewlRzAjUmjdCYayhSxiJrbp7OiMGtuHjyQB4p8b38GmubEpbx48eTmZnZYtsNN9xARkYG9913X5uTFQCj0ciAAQNYtmwZl1xyCeD94F+2bBm33XZbm4/XUUymWMyWBAwG36YmlqS2fy+E3w57eRmfP/kopTnZAKT0H8S0O+/FaBZrHAmCcPoEBCQzaOCn5Be8jctVSbeMecTGXoZash0VyFVjuKdzDM+X2AjQW/0aa5sSFpvNRs+eLcc0Wa1WwsPDm7cXFxdTXFxMVlYWAJmZmdhsNpKSkggL81azjx8/nunTpzcnJPfccw/XXXcdAwcOZPDgwTz99NPU19c3jxo6E0iSDG1afVlrXmNIEH5t+Zsv02i3c8m9D+FqqGfJ80/xxVP/5PK/ieHTgiCcXlZrCuld57bYNis2nA9zC/l02TLKSnKYtG8H5okXQ9o1/gmSDpjpdv78+S06xI4aNQqAN954g+uvvx6A7OxsysvLm8tcddVVlJWV8dBDD1FcXEzfvn355ptvjuqI619Si/WCBOFUVOQfIm3QUFIHDAZgyfNPcWj7Fj9HJQiCAGWHcpi2/nuiVyxDbqijPjwaa2MdqbXlJ965A51ywvLr+Vbmzp3L3Llzj7vPwYMHj9p22223nVFNQL8mIUNbExYxO65wDMm9+5G5/DtMVivOhnoARlxznZ+jEgThXLN8TymfbClAAi4fkMCorpHHLKupKv+55iIArCGh9B85hq4Tp5GYkMjb991B4El0+2hPYi0hX0mApvo7CuEcMWrmDWiqytZvF6MzGBh+xUwGX3SZv8MSBOEc8vnWAu78YCu9E4JRNY1rX/+Zl2b2Z0qv2FbLS7J3GgdraBg3P/86Ov0vUgRN8/tNuEhYfKS4wV5VTmnBvl+986tal6Z/T4/b2eYKGeG3w2AyM+H3s5nw+9n+DkUQhHPUe+tyGZsewcuz+vDD8pV8VbqDrz/aQXjjWAYNGtTqGnmRyZ2JS++OLB89B5W/Gw1EwuIje5mGK/QTqvZ+4vM+pQdddPXvPDuCIAjCb9SgyG9Jsn3Dgx/24Ks9k6jz9MSCC89XP/Lc9ufYathKpMXbRHR4kMhwu4my73LY9t1iAFRJQ5NAp0q44gP8di0gEhaf5a+eTWTnOnqOigdaqTz51YYt3+USnzL4tMQmCMLp5WyoJ3vjetxOJ537DSAoQsy5JPhHg6JS7HSTYDZg/EWtSEnZD2wLNfHf+mcx7CjDFKNyQehSdpVdwpLy7lyT52BrylYuSr0Ik75pXjMN1BA7SnENaBqaqnqbglTYWrKFtAyDn67SSyQsPrIFJxEVHUbXPl18Kr/x03XIkn+zUUEQ2p+9rJT/PfRX6qoqkWUZ+S0dl9z7EMm9+/o7NOG34MAK2LEIdEYWJ0zj9poIGhSVUL2OJzMSmRYZAsD/CvJZJk1iqs7AMsDZPYKPDddzp62OBeWwX+/9fHptx2sEGYOwGqwMihnEmPQxDBszjEBjYIvTXvnllWjB/pvhHkTC4jNJ8s5M6itN08QsLIJwDtrw5SI0TePm51/HHBjIc9ddwcePPcCfP/zK36EJ57ptH8Knt0BkBm53I5M2vMGfx75C7z4TeTGvlJt2HGRh4lZipTL2e7oToxUxMyaPH0jAuLsSd2QoCzMLASt/u/5C3srKI8YaQ1RAFBWNFfxYtJV3q/fjygpEky30Ntfxcq8MOgVGIEkSqp8HnoiExUfehKUNO2ggMhZBOPe4GhqwBNoIDAtDls+sGa0bGhoACAgQtbvnpHUvQvo0uPo9viupYNr8VGYvvxFG11BcX8rySliUv4/LDT+S6NxCkXQvb9TsYGDPan7e2xtjURUBIWHMv6of/RLD6Zf4dPOhvyit5iNPIQEeN8OMZaxosLDdEcjQDfmYtT0YHGF08fNAWZGw+KqNGYsGrfbAFgTh7NZr3CQ+euT/ePuvt2MO9Fabj/7dTX6NqbGxkUWLFjXPMJ6RkcH06dNP65prwmmgN0NjFXgcDPzFKh6v5pfx5qECIIj/G3wPkZbH0H46j1L3W3xrvJGaBIWxPW3M7RxLSpDlqM+mXXWN3LLzYPPrFZ6Wa5s5pEAc4X9gl5TXgRd3YkePWxJa1fYaFk3UsAjCOSihe0+umvsvolPSsAaHMu2OvzJg2iV+jen7778nPz+fSy65hAsvvJA9e/bw4Ycf+jUmoQOMnQMFG+GJVKKf64HHGMQNI97j4axCDJLK47pHCTV4O8ZKssQ0vmDHeT3JG92Hd/qmkhoc0OqNdPdACzNiW1+h/t3eKc3P08J7d8x1+UjUsPhIkqQ29mER+YognKvi07sRn97N32E0KywsJCMjg759+wLw5ZdfcuDAAf8GJbS/lDHwp3WwZzHoDOi7X8IbQd5J4Bobc/l5wwHWrB2DwRCC01lMly4P+FzT/1R6Ih8WV/LXTjF8XlrN7noHALO2H/k5ssj+TRlEwuKjqMzPCXtvCZnzfrm15Q+C9ouXfZDwmG+DS1JPS3yCIPx2JScns2nTJsLDw1FVb0eD/v37+zkqob00NDSwZcsW6urq6Ny5M12G335UImKxJDFk8FcUFn2Mx2MnPHwM4WEjfDvBlveQNr9FUuJ9vL3fjhk36MOPKmauKwTi2+GKTo5IWHwUbyzFFRmJMnTir+Zc0Vo8HGb47n+Em6pOV3iCIPyGjRs3DofDwYoVK5AkiYEDBzJ58mR/hyW0g4aGBhYsWEB9fT02m421a9cyatQoxo0bd1RZszmOlM53tP0kBZsgbz0PhK3iB1MKDejp4S6FpiV/JU1DbihniqMeGHSql3TSRMLiI5NOwdKnGwmP/7nV94tydlCeux/wth2yUo/BcmaNIBAE4dxkNBq55JJLuOgi78J1rU2rLpydtm3bRn19PbfddhshISG8/fbbrFy5ksGDBxMYGHjiA/jC5m1WmjZyFtMi0lovM38EBAxpn/OdJJGw+Eh1OtFHRhzz/QNXX01YjdJi29aG/Uzq6MAEQRCaiETl3NVho06XPQKrnvQ+rz6ER3FRrddjVFUKa3IIsMURaAmnoXwXMfED/Zo0iITFR42bNtG4aRNZP33dvO2XfVaiahSyp/Qg7ZqbQdP4+9q5jB/dww+RCoIgCOeK3r17s27dOp5//nlsNhuVlZWMHDnylGtXCuoKKGsoo5ejhsNtAdq7l3JfZDjfBVqP3iExnt87c7nzlM56akTC4qMfeksklGvo+zeNDPhVn5U6WabPzX8hsesAAIoPzkPSiSYhQRAE4eRZrVZuueWW5k63nTp1Ij09/ZSP++SGJ/k+93t0ko6Qbv2ID4gix36IWk8DQToLD6RcRqwtnob6UhyuWu7M+Zja2F7tcEUnTyQsPlp0eQx9o/rynzH/8am8pmli4jhBEAThlFmtVkaM8HHEj48kScKkM3HvoHvZVraNL7K/ACDRlsgrE18hPrDlaKBuNTvRxLDms0NkQCShplCfy6uozct1C4IgCMKZREJiQPQArky/kivTr2Tu8LnIyOiOs9yE9uumhdNMJCw+aqyuZnHJZ6zdthTw/mM3/e/wqxaPNeYa6ior/BOsIAiCIBzHd4e+A+D+VfcjIyNJEjpJhyx5n8dWhNIlNxaX0QMyDKhMwWL07xpVImHxUc+8ULJMbgKCgtA4snKz1vzf4Vfe9zoVBpAU1E5DzgRBEM4QVcX1bFmaS12Vk7i0EPpOSERvFP31zjapwalk12RTUl+CqqneL1Q0TUPVVGJz+9MlNwaAMlM1V7kmU1Xs8WvMImHxUXd7LGM7j+D839/mU/nnrr+ShJ4xHRyVIAjC6VNX5WTRE5swmvVEJAaycclBynJrmfJH/3bGFNrus0s+O+77mluh6ImNGGKt9LtxJCXPbyHYzzfhYtC+z9radqeJHiyCIJxTDmwtxeNSueqBQUy9tTddBkdzYGsZ5fl1/g5NaGelL2xFrXVh7tLUd1NV2/4x2M5EwtIWbchANA3vEs+CIAjnCFOAAUVRKT1Ui+JRqav0LpBnthr8HJnQXjRFxf79IdzFDQT0DsOcqFA+733chQ1IJVv8GptoEvJRyYEsKgryOP9m35qEEMOaBUE4x6QNiGL3mkK+eGard4MEo67uSmCoya9xCe2n4G+rm583bK+kYXslsmQjRP8SlphOwJV+i00kLG3gcTp9Ltu0ZFTHBSMIgnCa6fQyF93Zj7zdldRXOYlJDSYstpVZUYWz0uHBJAAGKYfAtAo0SyzmwX3Qf3cQTAn+Cw6RsPgsOiWN6JRjLArVGk20CAmCcO6RZYnkHuH+DkM4RS/nlfJyfhlRRgOXRIVwS2IUkiQReWsfalatZeYOif37uxIq1ZOxdxk698X8PlhijB9jFgmLj/aEx7NEsvHZl98gA7KmeR/RkLWmx6bXkgb5qT3pJ4kuQoIgCMKZZ0NNA/kONzVuhUezi9j6+ccMWr+FSTNvZ/gODxAGQJVmZa2zMwAxjYiE5WywvNdw8mQDZpcTTZLQJAlVkpseW77WkFBHT6ePTWKUvwMXBEEQTolj924qXnsdT0U51iFDCbvxBmSj0d9hnRJF0xgXZuOVnp24Z3cun/QYzsjKREpWFR5V1io7CMXBgIRkP0R6hEhYfGQNj+BqWwBPpCf6VL7zj9uwxcR1cFSCIAhCR3IdPMjBGTMxxMZiSk2l/IUXcGZnE//vJ/wd2kmpW7WKhk2bWNJnDAC/257DmmrvsHTNGk4wBdyc+jG59nhSQ3LoEbqXsODXSEpKIi7Ov59pImHxVZvHn0tofh6zLgiCIJwa+9dfIxkMdF70MbLFQu7vb8b+5ZdE3/tX9JGR/g7PZ4Vl9az+aB+ulTuw1ZVBH+/2EL2OOQlhGJavZ2RZDBIxjD80lsqUXYyf/jIm05lzjSJh8VFbx/xIkv8XihIEQRBOjS40DLWhgcbMTCw9e+IpLwdAsvh3XZ22uuexnxjpMEBoOtWh6bz34QrOe+oWzNam6+iShOpSqN1eRteQXoSlhfg13taIhMVH3nngfE9ZZEAV+YogCMJZLfiSi6n54gtyr73Ou0GvJ+6Jf6ELPLOGc2tuFaXGiS7MjOZRURvcSDoZSS8j6SX2BUu4cTHOYUTSS4z+z+1HrQElG3UEDzxzl5QRCYuPtDZOtS9LoHZYNIIgCMLpIJvNJL/9FvXr1uMpLyNg4ECMCf6dj6Q1lQv30ri9nKDzk3HsrcSVW9vi/S+wUGhWcf2pOyO6njnNPG0hEhYfaVrbmoR0SCiiE4sgCMJZT9LrCRxxnr/DOC7F7gLAvvRQ87aAAdGY00NB0ajeW0Hc1nKk/2Wh3heKbD77Pv7Pvoj9pL48m9zcXLbYo49s/NU8K9IvUpqh5aXIMRNOV3iCIAjCb5mqNfVFOLLJmBBIQG9vbUpAvygqlN00ZpZT9momkTf3RjbpWj/WGUokLD6av/sf9KvdAzt8K/8GsNF8P3SZ06FxCYIgCIKmaFgHxRA6vQuugjqUaiemX3WcDZ/ZDVdhHWULtlPx3m4iruuOpDt7Jjg9pUgff/xxJEnirrvuat7mcDiYPXs24eHhBAYGctlll1FSUnLc41x//fVIktTia/LkyacSWrvrZYLa3jMpu31Hi6/S2zJb/VIDIhhgOXt+EARBEISzmKI2Jx/G+EAsPcJbrUExxgUSdlU6zn1VFPxtNUq9+3RHetJOuoZlw4YNLFiwgN69e7fYfvfdd7N48WIWLlxIcHAwt912G5deeimrV68+xpG8Jk+ezBtvvNH82mQ6s1b/1EtgswRhC/dt4jjks6uqTRAEQTh7uYsbvF/ljUiyBLLk7bUgS82vPbpaigPeR1PcKOke0CR0qxqJnTzR3+H75KQSlrq6OmbOnMkrr7zCo48+2ry9pqaG1157jffff59x48YB8MYbb9CtWzfWrVvH0KFDj3lMk8lETMyZO5zK2+tWrGYoCIIgnHn0UQEotS5ko4ymAqqG6tG8fVtUDU3VaDTkUhb6uXeHpln2q/iWWLL9FndbnFTCMnv2bKZNm8aECRNaJCybNm3C7XYzYcKRzqYZGRkkJSWxdu3a4yYsK1asICoqitDQUMaNG8ejjz5KeHjrK4I6nU6cTmfza7vdfjKX0TaaelQn2+MTyY0gCIJwesTcM+CEZfLzd8A+GND/Q0pKF5Of/zYANTVbCQ7u28ERnro2d7L44IMP2Lx5M/PmzTvqveLiYoxGIyEhIS22R0dHU1xcfMxjTp48mbfffptly5bxr3/9ix9//JEpU6agKEqr5efNm0dwcHDzV2Kij800p0L1gNzW/E4MaxYEQRDODHv3PQSAotTTJe1+EuJ/B8DGTZehqmd+X5Y2fQLn5eVx5513snTpUsxmc7sFcfXVVzc/79WrF7179yY1NZUVK1Ywfvz4o8rPmTOHe+65p/m13W7v+KRF9YDO4Ht50XwkCIJwzsmpyeG93e9R6ahkWNwwLk27FN3x+iyW7YMDy8ESBhnTwOi/Kf0HD17Mpk1XsXXbjQCEh4+lW7d/kffMHLa+1Zue//kRY3iU3+I7kTYlLJs2baK0tJT+/fs3b1MUhZUrV/L888/z7bff4nK5qK6ublHLUlJS0qb+KSkpKURERJCVldVqwmIymU57p1ytrgStoQzVnoskGZAkGUnWI0kyzc0/kuR9LkmIlQ8FQRDOLXn2PK5ZfA02o42EwAQeWfsIWVVZzBlyjOkrti+ET2/x1s4rLghPg5uWQkDY6Q28iS0wg/DwUZSWLgGg1p5J2SorCZ95U4GK114n9t77/RKbL9qUsIwfP57MzMwW22644QYyMjK47777SExMxGAwsGzZMi677DIA9u7dS25uLsOGDfP5PPn5+VRUVBAbG9uW8DqUpLiQNr6JvPFNn/epazxIYMeFJAiCIJxGXx34Cp2k47OLP8NqsHLt19fy/p73ua3fbdiMtqN3+O4B6H4xTH8Zqg7CC4PgrYvg1p9Oe+wA9fXZzckKgMtdjtztK8AIQNHK1cTe65fQfNKmhMVms9GzZ88W26xWK+Hh4c3bb7rpJu655x7CwsIICgri9ttvZ9iwYS063GZkZDBv3jymT59OXV0dDz/8MJdddhkxMTFkZ2dz7733kpaWxqRJk9rhEttHYVIkUlAc5vgx3g64moqmKWiaApqGhtK8DU2ltGwp1s6dRcIiCIJwjggwBOBUnBTWFdIpuBO1Lu96Pfpj9W901UFYCuiNENLUbaEks/WyrdA0DWe9B6NFh9wOE7wFBHSib5/X8ZRmUvDos6g6ODAklboH8in4sjejbrnrlM/Rkdp9ptv//ve/yLLMZZddhtPpZNKkSbz44ostyuzdu5eamhoAdDod27dv56233qK6upq4uDgmTpzII488ckbNxXKgaxRx8ZcT2/kOn8rvXX0eFpOlg6MSBEEQTpfpXabzWdZnXPrFpZh0JlyKi7nD5mLRH+Nv/aCbYNVTkLseavLAEAA3L/fpXMUHavj+zV3UlDZiCtAzbHoqPUbGn1L8kqQjPHw0PHcRIdE6DiyJJjonFLc+nnGDUsgYP+iUjt/RJE07+ztb2O12goODqampISgoqEPOsXLVIJISbyQ5+Q/YazMJsvVq6r/Sup9Wn0dc3FWk+JjgCILQwRQ3bHgVclaBLQaGzYbwVH9HJZxlHB4Hy3KXUemoZEjsELqGdj12YVWFre/C/qZ+K4NvgegeJzyH4lF5+//WYAs302d8Inm7Ktm9pogLb+9DUo/Wp/tok+0fwSc3U19iJHd5BAAZO3cg6U7/hKdt+fwWawn5SFVd1NXv44flXZq39ev3LmGhx+mbc/bngoJw7vjiDtj+IXQeBfu+hR0fwx9XH6mqFwQfmPVmpqVMO24ZTdNwZWej1tdj7nk1Uv9r23SO2koHDXYX46/rRlKPcFL6RrJ7TRE/LdzPjHZIWBzdLuPjiQP4YvlqPrzydr41jqWbH5KVthIJiw80TUNR6igp+aLF9i1bZuFyRiPLcZgtnQgOTicyoidRUb38FKkgCK1y1cO2/8Gkf8KwP4GjBh5Pgjemwt2+9ykQzlCqCj89BZvfAVWBXpfDuAfaNhVFe4XS0ED+7XdQ37QcjT42lsT5L2FOTz+qbGFdIR/s/YDKRu8Q6amdpyJJErZwM9YQE+u/OEBjrYvcXZUAjLrm6GOcyH+X7uOZZfu5dlgyD1/UA0mS+HZnMQ98sYchnbtwkWsxkgRn1up9rRMJiw8aGxubn5eUpJC1fwixpmx6Z5ZgKqjCFZmFfdpuKhI/paIC9uxt2s/ReIwjCoJwWskGb/+B8r2geKAiy7s9tvfx9xPODj8vgB8eg4E3gN4Mq5/2zp016bHTHkrlO+/SsHEj8c8+gyEmhoMzZpJz8SV027O7RbmiuiKu+uoqZEkmxhrD59mfs7dyL/cMvAedTmbqrb344e3dfP/mbiw2A+Ov60ZCemib43lm2X4A3l57iFK7k292FjM2PRKAmGAzn28tJD7k7OhvKRIWH1gsFrZvn8CQwRdz6fRxfLN4ManP/oRJUbBNuJiGtesIWVBL8LsvU9ZwgNKy7Rw6uIHYmMH+Dl0QBPCO0jj/YVjyF9jyHqhuiOsHl77s78iE9rDvW+g6CS74r/f1uhdh7fN+SVhchw5h7NyZoIneBQVloxHV4zmq3JcHvkRRFb667CuCTcFc+/W1vLHzDW7ufTM2o42o5CCufnAILocHg1HnXcDwJPznyj7c89E2AL7Z6Z1xfvneMv6i/5Db9nxOQMy/ufV3s07yak8vkbD4QJIkau3xGAyRmM1mJqVncLCkhIR33iZg0CBcublkT5yE+7lX6fnf/1JVNYKVPz7DqFHJ/g5dEITDBt8MycPh4GqwRUPXKd5ERjj7RXT19k/aswT0TaNLzSF+CcU2biw1n3xC/l13Y4iOQnW7CZ4+/ahyRtmIW3VT46zBZrTh8DgA0Ekt+5IYzaf2MX1p/wRyKxt4+vv9zdvOlzdym967COK86r/Cc39Ffaga+SSTotNFJCw+kiSJwwOq9JGRIMvUfv89hqRkar/7DgDbmDEAqKoKeIds+4va4KZ+cylqrQtTagjmrm2vShSEc050D59GaQhnmTH3Q9E2+OAa7+voXjDjQ7+EYpswgdjHHqPy7bdx7NhB6DVXE3X33UeVm95lOgv3LeSizy7CarBid9m5f/D9BBhan7rfXdqAu7AOfYQFY0Irk9Qdx10TunLXhK5omsaLK7L5aV8IFY4V/FuXz/kNtZzX6GDrvlyGZpzZN9liWLOP5s6dS6BORy+r1dspansmwevWITV9++r69KFu1EhkgwElJYWVP/3EjTfeSFJSUofEczxKvZvS57eg1LrQWY0oNU6CJiQRNOHM/mEUBEE4aZoG5ftBUyAiHeRTn2ito9W76/k652sqGisYGjeUPpF9Wi1nX56L/dtDza8DBkYTelkXpJNcs275e3vYtaoQgEpLMdmhmfzrrr8THdR+awT6Sgxr7gDRJSVUhoayubERDdDiYrFMmkRwTTV1Nhv2oCC0phWptaIijEZjhyVPJ9KwpRSl1k3MPQPRhZooezkT+/e5WIfFobOe/l7zgiAIHU6SIPI4c6KcgawGK5d3vfy4ZZQaJ/bvDmEbnYBtbCINW0up/iwbY4KNwKEnt3zN4WQFIKwxhjA1xC/JSluJhMVH437eQMTs2YTfeMMxy7hLS8kaNZqE+S81Nw/5haohSSDpJSRJQtI1ZeFnf2WaIAjCb4pS7wYNTF1CkM16zF28zfvOnJqTTlgsNgONte7m1+bqWqq+X0rohPPbJeaOIhIWX+l03jWEjqO5ek71b2IQ0D+Kup8KKH5qE7pgI57SRmxjEtAFig6GgiAIZxNDjBVDrJXK9/dgTg/DmVODbDMQOj3tpI95OFlRlWpc9jdxAJ+8puMmkbCcIxQF5BN0om1uM/VvwqILNBJ1Rz/qfy5GqXURPLET5vaYzlkQBEE4rSRZIuKmntT+kIeroA5TaghB4xKRT2H0UEh0ANUlDWiqHfDeiFerKm6nA4PpzG0aEgmLjzRFQTrRapnSmdP0ogs0EjTu9Hf4FQRBENqXLtBIyEXtt+6V4lHpPzmZDZ8emYcotkv3MzpZAZGw+E5RvM1Cx3MGJSyCIAjCb5vHU09p6RIkSSY//10CAlKwWlPRh+Wzd79GzJjpZO3Mxmh3Me7G3/s73BMSCYuPNFU94UqWh/uwaOrx+7oIgiAI5zhXAxxa4+1KkDz8yIR2Haj04AG2f/81boeDtMHDcAd+RlHxoub37bXbMRjCiOxVh6x34XZ/jqFiPF2HTSUmJaHD4ztVImHxgaZpoCgUz32YjYfWIOt0yLLe+yjJSDodOlmPrsGJDVBUxd8hC4IgCP5SthfevgRqm4YPh6XCdV9AcMclBUVZe/nw7/cRGBaOOdDGrqeW03NqJ/SJ0LPHM0RGTkSWvQMvamur2bjpXxgMH5GRsYrOnf7QYXG1J5Gw+MDjcTU/D39vKZIGsgY0PUpNX4d7uOyWSxjul0gFQRAEv1v6dzBaYfbP4HHAglHw4jCYk9dhp9z+/bcERcVw3b+fQ6c38NTVF7BjyUH6/gGysp8kOvoCwHsD/tlnM4iL34vHo+dgTn9GnBffYXG1J5Gw+MCNwpVz9Pxr5L+YmjIVAFVTUTQFRVXwqB4UTaHKUcVFn17AE11i/ByxIAiC4Df2AojvD5HpR7Y57R16SoPJhKuxgcbaWsyBthaDVS2WIzU7dXV7iIvfB0Bx8dVceOENhIaeHUu3iITFB27VO2bdqDsyj4ksyciSjEE+MnOsQTagSRLqCeZrEQRBEM5hva6ApQ96a1c8Tu+2S+Z32Olc+bX06jeevWtW8cqfbkDWe7ssDJ01iAZ2I8tG6ur2ojcEs2XrtVitaXRK/gvjxo4/6en9/UEkLD44nLD8Mjlpja5pnhZFE31YBEEQfrOG3w56M+z4GCSdN1npe02LInVVDrI3lwHQuW8EQeGWkzqVq7CO0ue3AjAx5FryDXvxaG7iAlKxrQyjJl5Pafo7rK/wtg7oCaX/kHcxGiNO/vr8RCQsPnAp3j4sVc4qNE07ZkZ6eFlwkbAIgiD8hkkSDLnF+9WK0kN2PvvPFlRVQwLWfZ7NRXf2IzY1uM2nUqq9NTgRN/VENuuJdw1Gc6tobgXNpRLiTiXeeSl17p1U791ORNroszJZAZGw+ORwIvLg6gd5ZvMzvDD+BTLCMpAludVyoklIEARBOJZNXx8iKMLMpX8ZABK8ctdKPvn3JmbPH9fmY6kODwCmTsFIhmNPbmotiYfvIwiZ2POk4/a3M3/97TNAVEBU8/PyxnKu+uoqluUuO6qcJElISHhUz+kMTxAEQTiLeFwKRoseg0mHwXSCCUlPQG30gF46ZrKSl1fD9l2l1G8oAZ2EMTnolM7nT6KGxQfbyrYR6AxFp+pRJQVVUqiprifHcgidTofeoEOWJXSyDp2kEzUsgiAIwjH1GpvA4he38/7D65snSJ98y8nVfKgNHuSA1vtXpt3/JRnSQQq1CEYQzmNTuiMbTy1B8ieRsPggc+9+Zm2e22JbyWZYQnaLbYqkcK38GEq0GzJOY4CCIAiCX2iaRn11FSarFYPRt9lsO/WK4NI/92fP+mJQNUZfk058+skNLdYcnlYXQlQUlZcM/+V83SZ2q0kcGPEpgaPOjvlWjkUkLD4YHjqSb9hFymUmTFY9ikdFVTRUVUXxaGiKhqJoaIpM0VIdXUW2IgiCcM4rO5TDV888QWVBHjqDgUEXXc7wK2a0HJihaUfWmfuF2LQQYtNCTjmG+o0laE6F+o3FyGY9klmHbNbzxtKreT6tjNeLTHQbfTfdhqWd8rn8TSQsPtCp3m/TqMEDsQYfP4N+deVKdBy7yk3TNNR6t/cHSy+6EAmCIJyNNE3jq6f/hc5g4KI//x8lB7JYt+h/WIND6DtpGpTuhi/ugPwNEJII5/8Dekxv/zic3lGpVR/vb7H9+W65AKxPG8GgYTe2+3n9QSQsPvC4vX1S9D60/ck6CVVpfbVmV1E9Ze/sQqt0gEEmaFwitjGJZ9XEPYIgCAJ4nE4qC/OZdOtddBk8nLRBw1j/6Ucse/0l+k6YBO9fCcZAmPYkZC+HhddDUAIkDmrXOBIeHwmApqioDgXN4aGyuoIu3yVx0FzIjRe9267n8yeRsPhA8TQlLD7UiMg6GVU5utOtx62Q98JWHE6FvQ6FSIuezt8eQhdoxDpITOUvCIJwNjGYzUR1TmX9Jx+iuF0UZ2cBcOE9c6AmF6pzYebH0OV86HctPBoJn/0Rbt/UIfFIOhmdVQargfUHfmZ/QC6hWjABxoAOOZ8/iITFB0pTDYusP3FNiCxLqOrRNSx7VxYQ7FGp7ypRl/UlB3Pz6Jx8B1WL9ouERRAE4Sx04d1z+Hb+03z/2ktYbEGMv/FWug45zzsdf0A4rH7G24clu2kajPF/Py1xTetzMeo3ZfSv73Zazne6iITFBx63is4g+9R0c6wmoeoqJ7KmYthbQ4KhE13izwNgfdViol2Dfe5dLgiCIJwZQqJjuOrvj6MqCpL8i88IvQmufBs+uQXevwL0Fpj4GHS/6LTEJRlkBtR3Py3nOp1EwuKDwv3VKG4Vxe1B0snI8rGbho6VsHTuE8k3366iv9VCV30/XDqN/foyDlbvYNt3Sxh4Qft3xhIEQRA6nqxrpX9jpxFwV6Z35WZrJBhObq2gtlJVjZ2PreXsWH+5bUTC4oOCXd4FqubfvvLIRk1F0jRAQ2qaKE5CQ5ENROnKgC4tjhHXJYS+l/dl2esPYQ0ZSlKvoTjqdgDQud/A03EZgiAIQjsrdrqxexRSA0zofl0LL+sgJOm0xZKXt5F9+68iLHYaZF1BxA09Ttu5TweRsPjgwvPs7Hj+E4KmXwqShKZqeHMVzdtfRfO+1jSN+vUb6BQZAlx41HH6T+qPx3E9qz98l92r1mEwmZl6258Jj0883ZckCIIgnAK3qnHnnlw+KakCoJPFyBs9O9MtsPWaFFVVcbvdmEzt3/xfVFTEvn37qLHfg9UKH5s83Py3gZhtp6dW53QRCYsPDJ4GYss3kXHnmyfsx5Jz1UeYpa7HfH/wxZfTa9xE7GWlhMbGYbScOz24BUEQzjRFNY28ufogRTUOBnUO45pBieh1pz4H1rtFFXxeWsWT6Yl0shi5NjOHsRv2UjSmz1GfEz///DM//PADDoeDmJgYpk+fTnR09CnHcNiCBQsAMNv7Y42UeGzOi+127DOJmLnMB2pDI7LF4lOnW0mnR/Moxy1jsQURnZImkhVBEIQOVF7n5MLnVrNwUz5FNY38/fMd3Ltoe7sce09dIykWE7PiwhkRaiPC0Pr9f25uLkuWLKFHjx5Mnz4dh8PBSy+9hMdz8ovk5jY6+aHCzuv5Zdy0I4faEeORG+sxFBTh2lpAjd1+0sc+k4mExQdqozdh8YWk06EpYrVmQRAEf1uSWYS90c23d41i4R+Hc0nfeD7ZXMD+ktpTPva48CD2Nzi5ffchnsoppqSglqitVVz8wmqe/n4fzqYb17y8PAwGA9OmTaNPnz6kpqYCUFxcfNLn/tOuQ8zYfoD/21/A4rIa9ofFsLvvUIIMDu7O+Ini/T+e8vWdiU4pYXn88ceRJIm77rqreZvD4WD27NmEh4cTGBjIZZddRklJyXGPo2kaDz30ELGxsVgsFiZMmMD+/fuPu8/ppDY2IAX42Bao18EJaliEjufKq6V+QzHOQ3Y0rfWZh4Vzl1NVcapi1fTfOotBh0dVKa5xoKoa1Y1u7/Z2WLF4UkQwj3dN4OfqehZsz0PaWE6a2URaVCAvLs9m7he7AEhISMDtdrNkyRK2b99OdrZ30dxTaRLaaG8AIN5kYP/IXnzRvwv/u/pSbk7bgCxBrFp9ytd3JjrphGXDhg0sWLCA3r17t9h+99138+WXX7Jw4UJ+/PFHCgsLufTSS497rCeeeIJnn32W+fPns379eqxWK5MmTcLhcJxseO1KbWhA9rH5RtLp0RSRsPhT9ZfZlL6wlapF+yl7aRvVn2SJpOU3okFRuW3XIVJXbidl5XZm7zpE/Tn0+7h161Zee+01Xn75ZdasWYMqkrLjuqB3HD3igrnw+Z/o/fB3/LCnlL9OSichtH2a46+Pj2D9sO5cg4Xk8AA+/sMw/nNlXyICjfzv51w0TSM5OZnJkyeTmZnJJ598gslk4o9//CMGg+GkzlnpPlKDX+B083FJFVX1NfBwSPP2oJ4XnOqlnZFOqtNtXV0dM2fO5JVXXuHRRx9t3l5TU8Nrr73G+++/z7hx4wB444036NatG+vWrWPo0KFHHUvTNJ5++mkeeOABLr74YgDefvttoqOj+eyzz7j66qtPJsR2pTX43iSEThZNQn7kLmugbnUhwVM7E3heHHVriqhZfABj5yCs/duvk5twZnoyp5jFZTU8mBqHBDyUVUi9ovBmrxR/h3bKtm3bxmeffUZ6ejpGo5HvvvsOt9vN6NGj/R3aGcti1PHxrcP4Zkext9NtpzAGJB97hhJN0ygqWkhxyZfIkp64uKuIipp8wvPEBFsosTtYd6CCpPAA6pwtPwOGDh3KoEGDcLlcWHz9LDmGMIOeRX1Tsel1/OdgMXP25fOCo5iNvyxkORdnYTnJhGX27NlMmzaNCRMmtEhYNm3ahNvtZsKECc3bMjIySEpKYu3ata0mLDk5ORQXF7fYJzg4mCFDhrB27dpWExan04nT6Wx+be/gDkaFZZ/QMF6l4J2+yIoOSZWRFB2yqkNSZIz2QEKyE0GWqP9xJYHiD4jfqPXeKl9jkg1JJ2NMCATAXVTvz7CE02RDTT2TI4L4Q2IU4E1Yvik/Nzog7ty5k06dOnHNNdcAkJmZyfLly0XCcgImvY6L+8b7VDYv/03273+U8PCxqEojmTtm07PHs0RHTzvufr8blszSXcXMeHU9ADaTnv/dPLTFQA2dTnfKycph54XaAHizVwqZtQ18UxDJTZtms7e+K9eN2cfN7XKWM0+bE5YPPviAzZs3s2HDhqPeKy4uxmg0EhIS0mJ7dHT0MTsYHd7+6/a84+0zb948Hn744baGftKUSTG4IwuwFIei6hU02YMiK3hkJx5zIzWmIsIKuqCpKoFjxhA0beppi01oyZhoQx8dQPlbuzCnBuPMsaMPNxM8sZO/QxNOg/7BAbxTWMHr+WXNHxZjw2x+jqp9BAf/f3v3HR5FuTZw+DezPZtN76RRAiRAgFBDESmCqAgoFmzYe0WPymfXYzlHz7GLvaMiKhakg/TeewvpvZftZb4/FoI5CSSBQAK893XlSjL7zswzO8nus2/1Z8+ePeTk5Jx0c4JwYvn5vxARPp5u3f6Loigs/asTu3Y/2GjC4qtT89Pdg9icVU6V1UnfuCD8fU7fPdqWXcHWrHI8CmzJLOfPnfmAd7mXjuWVp+28ra1ZCUt2djYPPfQQixYtQq/Xn66YGjVt2jSmTp1a+3tVVRUxMadv8jVtlw6EqnqQPOL9eo9lZn1Kevq7RL/7zmk7v9B0kkom9M5kqv/KxllgxqdnKKbhMUgaMSDufPB4fAQFdidPH8xFAcaFBvBW13NjYsYLLriAjIwMPv30UwBMJhM33XRTK0d1btFpQ6ip2YfNlofbbQVAlrVN2leWJfrFB530uW32AnbuvA9QkGU9KpUPKpUBlWzA5aqmxnyA8PBLURQ9Ez6JO+5xdspdGXHSUbRtzUpYNm/eTFFRESkpKbXb3G43K1as4L333mPBggU4HA4qKirq1LIUFhYSEdHwisRHtxcWFhIZGVlnn169ejW4j06nOy2zBR6Px+NAo2m4TdDhKEGlar3kTahPZdQQcNnZ32dBaD6jWsVH3eJ5s6sHBQVjQ2u8nKVMJhN333032dnZuN1uYmNjm13T4nJVc+jQvygrX41WE0x8/L2EhJyrb2/N17Hj42zddhOr1wwFQK+PJqX3jDNy7urq3VRVbSMifAIKHtxuCy5nFXZPAZWVWwDIyfkOSZJIjRrPlsIkVJILh1uHSzn2Vp5uPncX0m1WwjJy5Eh27txZZ9stt9xC165deeKJJ4iJiUGj0bBkyRKuvPJKAPbv309WVhapqakNHrN9+/ZERESwZMmS2gSlqqqK9evXc88995zEJbU8xeOol2UrikJm1sdkZX1KVORVrRSZIAgN8WmBmUzbIpVKRXx8/Envv3v3VCoqNxEZOYmamn1s33EHffv+gr9fz5YL8ixmMiUyKHUpZWVrkGQ1QYGDUKnOzPT2TkcZAImJryHLdRPR4uLF7Nh5FxqNP/36ziZ1IKjVJhTFyfods5g3+yA2RU2aJ5grB5ybI4SgmQmLyWSie/fudbYZjUaCg4Nrt992221MnTqVoKAg/Pz8eOCBB0hNTa3T4bZr1668+uqrTJw4sXYel3/+858kJCTQvn17nnnmGaKiopgwYcKpX2EL8CguJKnuU1VWtoK0tH8TF3cPHTtMPc6egiAIbYPH46CkdCldOr9AdPQNKIqbpX91ZtOmKxg5Iq21w2sz1GoTYWFjzvh5Hc4y1Gr/eskKQGjoKEymZKqrd7BiZW8AJEmNVhuGzV7AZt+7MLtC+Pbuq1tsyHZb1OJrCb355pvIssyVV16J3W5nzJgxfPBB3XUN9u/fT2XlsY5Bjz/+OGazmTvvvJOKigqGDBnC/PnzW7WfzN8pigtZ0vzPNu/8B+2irkWSzs1Pc4IgnDskSYNeF0VR8XwCAvpRXbMXgHbtbmjlyAQAp6MUrfb4fWBSen/D1m03YTDEI0ky1dW7sdlykfAwtc90hl+4H1k+t5cHlJRzYEatqqoq/P39qaysxM/Pr8WPv2btcIKDhtGly/O12woKfmf3nkcYOmQ9Wm1Ii59TEAShpZWVrWHnrvtxubwfGMNCx9Kt25sNfqoXzqylfyWiKA6iAt5GozWi1fmi1Xm/+wVHIMtqZLn+h+MlS71T/ffvNweTKfFMh33KmvP+LRKWJjj6BxHg3x8kCZCwWtJxe6zExtwGkoSEdztImExJBAdf0OJxCIIgnCqXy0x19S602hCMxo6tHY5wxNH3mRMxKFMw+hvwqHahUvsSFDgIJIkDB14kKupqunZ58QxE2rKa8/59btcftRgZ8FBR+fe5BCXUah+yc74GjuV8brcZnS6SQalLznSQgiAIjVKrjQQGDmjtMIT/UbbhewIi3PS+OByHzYzDXoPTYSYz4zs8mk2otGas0ldYq8Co749GX87+A8+jVvsiSRI2W25rX8JpJxKWJtDrI4kIH0/Hjo82WvbAwZcpLT03V8oUTkLhHpj/JBTshNAuMPqfEN23taMSBKGNsZndGP1CCYmoOyVDQo9js8DnHCjkj3c3MPKmfnROicBqzSU/fxZWWzbxcW1jVO3pJBKWJlA8LqSmtvEqHtEJV/ByWOCbCWAIgoH3wv4/4dOR8NhB8A1r7ehOyuHiGjZmlBHmp2dopxDU5+jwYUE40+xmJ3rjid9nojuHE5cUx7Jv9+Mf4kN4+3Z06PDwmQmwDRCvNk3gUZzIUhNzO0lu1ZWBFY+CZUcxlQsysGwvQnGf9V2Uzl4FO6CmECZ+CMP+ARM+9G6fP6114zpJP2zIYtR/l/PEzzu55YuNXPfJemzOc2clZEFoLR6Pgt3iajRhKd+TzojrOuG0u5n35lo859BK5E0haliaQDkyD0tp6XLSChaxQzMKH8mJpNhRSSpy7W621zjoZTJwqVbi731azmycCqXf7sW2pxSVnxZ3lQN912KCpyTVWYRLOEMC24OsgfUfQf87YNuRGTNTbmzduE6Cw+XhpTl7uDIlmpcmdGdbdgXXfryOJ3/ewVvX9m7t8AThrGa3eBdtPVHCMufuT8mkA7J7P6i0eCorcOXkoI07/jT95xqRsDRBnsvAlrQFpHGIn6Tr6j0u48aDivlWeBt/kuVYfmuFOJ25Ndj2lBJ0XVd8kkMxby6kfNYBzOvy8U2NaoWIznOmcBj3Nvz5KGz/DlRaGPMqdLiwtSNrNofbg9nhJjnaH71GRfd2/gBsSC9r5cgE4ezhdtvZvecR3G4LKlmPrNKjkg2U54QDySz/cRPZ6ZWoNGo0Wg1qjRqNTos9q4ZMvH1bQku2EVKyk3BdOZroSa17QWeYSFia4Cn+jUXyrf395ggDU+MjUSQdLo8Dowxl9kp+yNzKOos/Wa5urRKn4vHW7Mh6722Vdd51VDz286vasE3pfT0kjoOyNAiMB0PDa1K1db46NZclR/LSnL2sOFjC/oJqfHVqZt7V8JIbgiDUZ7VmUFy8gMDAVDyKE6e9Co/HjtVWBCRjKdexe3EQiud/35q9HxBisxbTM6oYn0suJPiWW5DOobWymkIkLI3wKAoWyRc/WeHaAB+ynR4u0unZXVDB0UYW+UhzywX+vcn3WMiqcbRKrNpoE9p4P0q+2o0mwoizwIw2zg/TBdGtEo9whN4Pos7+ZpM3rurJZ5HprDtcSv/2Qdx5QQdigs7dacAFoaXZ7UUAJHZ9DYOh7uvyyHHHflY8Ck6nE6fdhsNuwVZWxt6FNxB580hiB35yJkNuU0TC0oijvVGqPBIfl3mXG59XbT/hPoF262mOqmGSLBF6W3dqNhTgKrDg0zsMY/8IJFn0XxFOnV6j4r7hnbhveKfWDkUQzkoORzEAWm3oCctJsoRWp0Wr02LED0Ij2JNbjVWXfybCbLNEwtIIGbhx7XwSUwfRPj4ej6LUJjHKkZ89R0YFKQrsP3CAsoP74eLWqSqXNCpMg9u1yrkFQRCE47M7ilGr/VCpdE3ex+VyUFJykJDgkXg8ltMYXdsnEpZGKIqC0WGjd4CJ3u1jGy1vzM9mncN2BiITBEE4+ymKQrm9HKPGiK4Zb+Rno6KiebhcVVRV7UBRtDgcoNP5cejQHAoK5yChQ5I0SLIeWdYhyzqczsVoNN5ae6Px4la+gtYlEpZGHJ1TpanDgsXwYUEQhKY5XHmYJ1Y8wb6yfWhlLVO6TeGB3g+cs6+j1dU7Adi4aWK9x3Q6sNliQXEieZy4JSeS5EKWXVjM/hQXx9O37+VnOuQ2RSQsjTiZhOUcWE9SEAThtHtyxZPY3XZeH/Y6B8sP8vGOj/HX+TOl25TWDu20GH7hPsyWNFAUfv11JlqtQkJCLE6XmZjoC4iK6lVbtqamhmXLlrFh82aCgoK49NJL6dChw/EPfh4QCUsjmpuw/H0fQRAEoWEuj4u9ZXt5ZuAzXBx/MRfHX8zHOz7mjU1vnLMJiyxrMPl2BaCqKpQOHTqQkjK2XrmysjI++eQTFEVh1KhRDBgwALVavF2LZ6ARooZFEASh5allNT1De/L5rs+RJImD5QcBeHHQi60c2elns9mwWq3odA332dm8eTNWq5XHHnsMX1/fBsucj0TC0giPxwPA7h37kByNdwgrzC1B5CuCIAiN+9cF/+KZ1c/w4toXMWlNPNrnUSZ0mtDaYZ1206dPp6am5rjJSEJCAqtXr2bFihVcfPHFyLJY9g9EwtIou9U7Cdy+g7vZd3B3k/ZRKed2T/ez2ebMcj5ankap2cHgTiHce2FH9Jrza7ZIQWgr2vm24/Mxn+N0O1HJKuTzYKV7h8NBZWUlAwcOpE+fPg2WiY+P59JLL+XPP/9kw4YN3HnnnURFieVVRMLSCK1Wj19Zd7oPjSY64cST/QBk7iwhY0vVGYhMaK79BdVM/ngdHcN8SQjz5aPlaWSWmnlbLN4nCK2mqtTKqh8PUnC4ElOQnoHjOxKTFNTaYZ025eXlAKxbt45obSganRatQYtGr0Oj16L18X7v2bMnvr6+zJw5kxkzZvDQQw+h1WpbOfrWJRKWRsiShM4RRIcOHejUK6zR8vZiDTmKmIelLfpzZz5GnYpf7xuETq3i2o/X8tu2PJ4b140g4/n9QiAIrUHxKPz5/g6cNjc9Lowmd385v7+zjeueH0BghLG1wzstgoODUalUuN1uflrxR5P2MZvN/PDe19w09fbTHF3bJhKWJmryICGlOYWFMynMpKPa5mJDehlJkX4UHVliwUcrmoQEoTVUl9koyzNz6b3JxCeH0Ht0LB89sJw/39/BDS+dmwtrqtVqnn76abL+ux57cQ0BtybicDhw2hw4bU6cdjtOuxOn3YHD4cRRZaU0s5DY6sY/MJ/rRMLSiOZ2oFVQRL7SRk3qE80f2/O48bMNAGjVMh/ekCL6sAhCK/Hx06I1qNm9Kg+Dn5bsPaUA9B7d+KziZxuH1YJaq0NWqSj5bBeqYic+6AiJi0DW1X8NcuRUU/7LQZx5WhLwx3ewWMRWJCyNcDu9o4SsNc6m7SBGCLVZeo2K7+8YyIaMMkpq7PSLDyLcT9/aYQnCeUutVTHqliQWf7GHn17bhCRByphYkoacOx1MbeYaPrnvFhxW7/T64x95Cv0hF/qkYAxJwQ0mKx6ri+KPd6IO0RN0fVd08f6oTKLZWiQsjbCZvYlKcWYVIBYVPNvJssTADsGtHYYgCEe0Tw7h5tcGU15gxjdQj4/fufXGvG/V8tpkBSD9y9UkBgzAOCACQ5eGOxe7K+0oDjcB4zqia+9/pkJt80TC0gi9rwaA6MSm9VqXVRIej6hmEQRBaCqNTkVYnF9rh3FamCsr8A0K5q7pX+G02yh8biMA2Z+tx+5rI3bKQEztwpDVx2paarsiqET/gr8TCUsjFMWFvfIzfn/dfJzOtP+zTXGh0kQBw85EeIIgCKdNYeGf5Bf8AihEREwkInzciXdQFKjKBVkNpogzEmNbdXhbMTuWZpO1fRFOm7dvjkanRzcxkvKlafhU+uJj8aV6+iEKPFvQDA5EpVOjMmgJjPD2V5FU5/68NM0hEpZGyJITxVNJVJdB+Aa1A5RjU+8f/VabDivk7FmBrSanNUIVBEFoMfn5v7Bn7z8IDBgIksTu3Q/jcduIirqq4R1qimDmjZC9zvt7whiY9BnoTGcu6DYkfXsxZflmnLYSAP5zzWVofdpxwfW30uWBoeByU7zlEPb5hRhlP1jrBtyAnXL2gkZCE+HTqtfQ1oiEpRFHk5H+40fSsc+ARstvnR/G8m8/P91hCYIgnFZ5+T8RHHwhvXp+BsCSpR3Zu+/J4ycsC56CssNw9dfgtMLsu+D7yXDznDMYdetzuz3YapxYqpyExvox5o4P2LVsHbuWzMBhyWXxJy+xe/lwrnvpUWJG9MZzgRuH2YrLYsdltZH78QYMbl8q/SuIFjUsdYiEpRE2cw2+6gDSFq4lpnN3tKZGJjOSpOaPhRYEQWhjtNpgqqv3YLXmNm3x18Jd0PUSSBrv/X32XZCx8vQG2QYt+HgX6du9tSpdB0bQrnMs7TrHMvKWKyjLLWbGU1Nry1YUFpB3YC92cw0qtQa9ry+Z7dLYu2oZN9z7dmtdQpslEpZGyC6ZS2PugkooenkLVZ4ylGQdiZMvQlbVH44mSbJYrVkQhLNeh/YPs2Xr9axZewEAGk0wKSkzTrDDhbDpCzBFgtPi3TbkkdMfaBtTVWIjvkcwiYOiCO9wrCOxWqMmLD4SxePCXFHIH2/+iwPrvAmdSqPB43KjKB58A4MYdsOthLfv2FqX0GaJhKURpoAQzBzGEeXBUlRGgCsEdsGON36l1xNX1ivvrWARCYsgCGc3o7EjqQMXUVq2ClAIDhqKWn2C/igjn/U2Ba1939vpdvDDMPzpMxVum2G3OmkfE0KH3vXXnlMUBUkVQVXRHszlei668366pF6AzscHt8uF3VyDwc+/aTVa5yGRsDTmSPIRdXEy+s6BVKTlUvPJYULKw7CUVOITUneMvCTJYvI4QRDOCWq1ifCwsU0rrDHAuLe8X+cxu8WF1lD/rbUsz8yKH/aj9Z1IeHsPF16XTEjMsen2VWo1Pv4BZzDSs49IWBrjOfL9SMJbmZbH0Yagkte3YU1y0mXKqGPlJVAUD4IgCMK5IXPHNsoL8lBrNKi0WtQaDWqtzvu7Rotaq0Wl0SCrNDhtbnQ+x95aPW4Pm+dnsunPDEwhesY90JPYbmLyypMhEpZGlJVa6DvGRNf05byx7ycA3L1dIEtITgWQWPLLf/HIHhRFwWm1EtFXrNYsCGeLrVnlzNmRj1olcUXvaLpEnD3DcKvLSji0YS2KAgn9UzEFh7RqPIpHQZLPveaMOW//C1tNdeMFJT36gHsxVxQAURRmVLH6p4MUpFXSZ2w8fcfGo9KIkT8nSyQsjSg2eZ+ifZp4XC4bkgIYFRSPB0UDHqcTt7sKt8cFEqj9bEREVOPxuJFlsaieILRl83cVcM+MzUT5G3C6PXyxKoMZdwygX3zTZrZuTYWHD/Hji9Nwu1wArPr+K6569mUiO3VpkeOXf/89JZ98gruiEt8hQ4h47lnUwQ3XDNgzq6iYfQhngRl1sB7/yzsed9r5s43b5cRWU82Yux8i6YIRuJwOXA4HbqcTl9OB2+HAdeTnzJ3pbFviXYNuwSe7OLS5iMBII+Mf6U27zoGtfSlnPZGwNCIxTMf/7XmOYsMFDBz+U6Pl8/JmsXffk2dsxWaPx4HZfAiNNgi97vyeWVIQmuujFWkMTQjly5v74fIodH56Hld9uJaM1y5t7dAatfbnH/ALDefaF/4FSLx3y9V899SjPDrz1Oc9qVm+nIIXXsR/4kS0HdpTMv1Dsm6/g/a//FyvQ6jH7qb0q92oQwwEXNEJ665SSr/YTfijfdCEnv0Tn1mrqgDw8Q9AVqnQqgxo9YYGy5Zk24BKNs+vwsfPxoibutJlYCTyOVjr1BqaVTc1ffp0kpOT8fPzw8/Pj9TUVObNm1f7eFpaGhMnTiQ0NBQ/Pz+uvvpqCgsLT3jM559/HkmS6nx17dr15K7mNHC7bXRjFw907Fa7rahoPgUFv6Mo7vo7HPlnPhMjhSort7B6zTA2bBzH6tWD2bt3WsMxCYLQIJUk4XC5cSsK7rNsDTCn3YbB14RWb0BraPgN9GSZ16xBExND1KuvEHLHHWjj47Dv3QvO+qvWOwvNeCwuAi7viG//SAIndAKgcn5Gi8bUWixVlQAY/E681pHDZmXX8r21v1/xjz4kDooSyUoLalYNS3R0NK+99hoJCQkoisJXX33F+PHj2bp1K/Hx8YwePZqePXuydOlSAJ555hnGjRvHunXrkOXj50bdunVj8eLFx4JSt52KH7fbO5+ArNIDYLPls3PXfQDs3vMI/v4pGH064eMTj69vV6TatYVO74ufoijs3v0oBkM0Pbq/S3XNXg4ceB6NNohOHf9xWs8tCOeK+0Z04o6vNtH/5cW4PAoGjYof7hzY2mE1Se8xl/HbG//kq3/cX7tt3NRpLXJsXeculH39DWVff422fQecuXneBxqYe0oTYkDSyFQtzsI4MBLrtiIATMOiWySW00FRFJw5Nd6+iGoJVDKSWkJSyUgqCdQyHjyoNOrahMXHL+C4xytMT2Pee/+hqriG2F43cen9w9FoRZeAltaszGDcuLoLX7388stMnz6ddevWkZubS0ZGBlu3bsXvSCb61VdfERgYyNKlSxk1alRDh/QGoVYTEdE2mzMcjmIAaqr3odNGUF6xvs7jlZVbqKzc0sCepzthcWC1ZREbdwcBAX3x90/hwIHnycz8UCQsgtBEw7uE8eeDQ5m7Mx+NSuLynu2IDT47mjE69RvI1c+9yp4VS1EUhRG33E1s9+Tjlj+0uYgdf2XjsLlpnxxC30viUakb/iDpP2E81u3bKXz1NVAUtHFxtPv6a6QGEhbZR0PQdV0p//kgti/LkPQqAq/qjC627a6+7MioovijHScsU2jNYFnBTG+tuSTh4+dfr4yiKOxcsoClX3xIUHQs1738EiExcacr7PPeSVdluN1uZs2ahdlsJjU1lbS0NCRJQqfT1ZbR6/XIssyqVatOmLAcPHiQqKgo9Ho9qampvPrqq8TGxh63vN1ux2631/5edaSN8XSorNwOQNrh10k7/HrtdpXKl8GDVlFcPI/MrE9ISvw3GRkfIMs69PooJElz2mICkGUdgYGDOHz4LRz2YmpqvFWRvXt/e1rPKwjnmi4RprNqZNDfxST1ICapR6PlMneXsuCTXcQmBREQ5sOWhZnYzU4umNxwB11JpSLyxRcIfeRhPFVVaGJikE5QS25IDEY/LQh3jQOVUYN0nESorfBYvB2VQ27tjqRTgduD4lJQXB4Ut4fsXzfh4/Zn9F0P4nG7MAYGo9Hr6xzDabex5LMP2b18MT0vuoQLp9yBWnN6X/fPd81OWHbu3Elqaio2mw1fX19mz55NUlISoaGhGI1GnnjiCV555RUUReHJJ5/E7XaTn59/3OMNGDCAL7/8ki5dupCfn88LL7zA0KFD2bVrFyZTwy8ir776Ki+88EJzQz8p/v69AAgPuxSDTzwZGe8D4HbXsGJlL2Kib8ZgiEOSNURETKCicjNBwReckZkKu3d7iwMHXiQn91u02mC6dXuLoMDU035eQRBOr0Mb17Fm1gxqykqJTurO8JvvxBR08kOW0zYXEdzOyGUP9ESSJA5uKmTn8tzjJixHqQMDIbBpo1sklYTaX9d4wTZAcXnnytLGmZB19d8Giz5Mp72pB/ZykHUa/HzC6zxeU1bK7H+9SFleDhff+wjdho08I3Gf75qdsHTp0oVt27ZRWVnJTz/9xJQpU1i+fDlJSUnMmjWLe+65h3feeQdZlpk8eTIpKSkn7L8yduyxWRSTk5MZMGAAcXFx/Pjjj9x2220N7jNt2jSmTj22gFRVVRUxMTHNvZQmcbutAFTrulGp6Ywq5lncihtyXgYgO+dLAEpL/6rdJyfnKy4ctguVqmU7wv0vrTaY7t3FAlmC0Jbk5+ezadMmnE4niYmJdO3atVkfYIoyDvP7f14hvmdvEvoPYuuCOcx66Wlu+e/0k/4g5Buk5+DmInL2leMbqMPjPrs6GLc0xelNWI5XE2R1e+dc0W0EcFK6Yg/B/44HvDUrP738DHaLmckvvU5YfIczELEAJ5GwaLVaOnXy9gLv06cPGzdu5O233+ajjz5i9OjRpKWlUVJSglqtJiAggIiICDp0aPoNDQgIoHPnzhw6dOi4ZXQ6XZ2mp9PpYMk2ACzZ/27yPrKspZkDsARBOAfk5OTw+eef4+/vj8FgYObMmYwZM4bU1KbXfGZs34Jap2PC488iq1RUFhWwe/kSitLTCO/Q6aTi6jUyhuw9pfz+9jYADCYNl97b86SOdS5QXB7v7OXHGcFz0fTHcVnsOC020j9Zibbi2Fvl3Hf/Q2VRITe8+ibB0cfvuiC0vFMejuPxeOr0JwEICfFWXS5dupSioiIuv/zyJh+vpqaGtLQ0brzxxlMNrUXYAifwSXYVt3e+kEiDCZ2sRiPLLC+38OrhAtandkcny4AHRfGutqlWG1Gpzo6qUUEQWs7mzZsJDAzknnvuQa1W88ILL7BgwYJmJSzB0TE4bVY2z/2Ndl2SKEg7CEBgZNRJx6U1qLnisT4UZlThtLsJb++HVt82RmPa3XbUkhrVGZxoU3F6kDTycWusJElCY9SjMerxSG5ckrdGavFn0zm0cS2j73pQJCutoFl/sdOmTWPs2LHExsZSXV3Nd999x7Jly1iwYAEAX3zxBYmJiYSGhrJ27VoeeughHnnkEbp0OdZOOnLkSCZOnMj993uH4j322GOMGzeOuLg48vLyeO6551CpVEyePLkFL/PkuSQ9y6RR/DckiSi9tna7ZC6hQnJi0oeJlTUFQQBAo9Fgt9uxWq3o9fqTmo+pQ0p/eo8dx4oZX4CiYDD5Menpf6I1nNroJUmWiOhQf6RLa1qdu5q7F98NgFpSo1PriPCJYOa4mehO44c+xeFGauqwY6eCR/KwZe5vbF/4JzFJPegxYvRpi004vmYlLEVFRdx0003k5+fj7+9PcnIyCxYs4KKLLgJg//79TJs2jbKyMuLj43nqqad45JFH6hzjaJPRUTk5OUyePJnS0lJCQ0MZMmQI69atIzS0/tLcrcF15AVH8z9Vh1lWBwpgcXswqsV4e0EQIDU1lT179vDmm28iyzKyLHPNNdc06xiSJDHi5rsYMOFqasrLCG4Xg1qrbXzHs1CngGNNXC7FxYioESzMXMir61+lf0R/LulwSb19HFlZODIyUIdHoDL5IptMyEYjlW4PVo8HtSQhISFL3oZ5b8uP5P35yHdrdjWeGieHMstRqWRUKgmVSkajllBJMpoiK56DFdjTK/G3BFJgSWflVz/T++JxDL/5zjP19Aj/Q1LOxJSsp1lVVRX+/v5UVlbWzgHTUv4sruC2XRnsHdKdQM2x/O7TnGKePpjLZaH+hGk1PBIfTqhWDGkThPOdxWJh7969OBwOOnfuTPBx1t85UxSPgnV3Cc6cGtQhBnx6hSG1oQX41uWv446FdwBwR487+GTnJ7WPbblhC7LZimww4FKpUaOwL6lbvWM4LxrNpVfeirOJb2ebFjS+kKHsp0XfKQCH3kmpkkdoz06ig+1p0Jz377bRiNmGOY9M1635n2afayOC2FVtJcNqZ2V5Db8VVfBUh0iuixLLhgvC+czHx4c+ffq0dhi1ymcdwLK1CFWgDneFHfPGAkLvSkZStX7SYnfb+ffGfxOkD2LOxDlkVmXWSVgKn1rHgZwFXHfbJFA8/PudV+h35LHQhx6k+O13AKgOj8SpKDzbMYpOPjoUwKMoeACPQu3vR79vjHfhm28l2KTD4/bg8YDb7WFmXin5VgcDO4TwcL/42ub+CLqfyadFOA6RsDTiaJOQ+n8SFl+1ircSvZ2uCuxOXkrLY+r+bL7MK+Gu6FCujDg3VioVBOHMqqys5I8//iA9PR2j0ciFF15ISkrKSR3LVWrFsrWIgCs64ds/Ent6JcUf7aByQQYBl7RubYHD7eDCH0ZQXhZDB9MIXl28lJwyOzH2xzhUuQuV8SBPxr6FT5J35u7LF82kWGdjc1w4fTILKctchmQwoFitqAZ5OzUPDvSlp6mJfX3aH/tRURReSMvjhxqFF1LiuCsmrKUvV2gBImFpxK5qC6FlpeSmZ3jbQVWq2nZQSSUjSTKyJPFskJ4RKn8+yy3hkZ3VTAwPRBadcQVBaKZZs2ZRXV3NqFGjyM/P5/fff8doNNYZvNBUR1tIJI3qyHdvrcrReUjOpF25lbg93loOi93FzH2/UZQ+AXdNEnuAPftAUjnRahUc1guRzR3Z3v59vk7LIaTGSnlcRzi8u3bCCPevO0CtJvq9d6np0xe2HER/gjm/TmRuSSUfZhfzckI7botuG/0nhfpEwtII16JF/PjOv3E0oWwS8B+gODgU+aIVpzkyQRDONQ6Hg5ycHC6//HJSUlJQFIUdO3bw/fff8/zzzzf7eOpgPfpuwZT/uJ+a5Tk4SyxoIo0EXHpma1c2Z5Zz/afrsNVJlCKQVCbuH6vmntRhgIRepSG3wsbY9+ZiV1fyn8Fv0HvKGHof2cM6ZSyZzz6AklkIQOdVK1EFBGAr8/ZJ0Z9gZWSXR+GnwjI2VVo4ZLGxq8aKXpbpatRT4HASpdOIZKWNEwlLIx4yqigBqt5+D4/HA4qC8cMP0BzY32B5RasltKLszAYpCMI5QaPREBQUxMaNGzGZTLXLmhwdidlckiQRfF1XLFuKcOTW4NMnHGP/8DO61o/L7eHaj9fidCvcMbQ9E3tH46tTo1a70WndBBsCast9vjqD/y46QJBvIG9fdz19Y+qu+GwwxNDl37P5df9L5EUN4cCnB9AH+LDTDwgFLfUTlkyrndmF5cwsKCPd6qC7r4H2Bh0Pxvnh8CjsqrEQI2t5MC683r5C2yISlkYUfvExKqD8v//AM7QvSWMmYz3kncjJ75JLQJaPrOYJruJiLGvXtW7AgiC0rqz1kLESTJHQbSJoG+9ToSgKW6ssZFht9LxkJIdWfMTSpcuorg5n0KBLGDRo0EmHI6lkjP0iMPZrvOzpUGF14nQrvH9dCpcmR9Z73OHyMGdHHtOXpXGouIabB8Xz2OguGBtY4we8SVjHm8eRt7CcsmIHgSoN6QU1EGpC/bclB/abbfzrcD5zSyrxUclcHOLPx93i6dHUPi5CmyMSlkZkjEoi5oeVeAL9iP5+JdZvV9Y+FvXvfyGp6z6Fli1bceZkn+kwBUFoC1b+F5a8AHp/sFfD2vfgtoWgO/5q0G5F4c7dGSzOmI9f6bd0yq3EV9IyLKmG3gluoq1RoFxEA5UHbUdVPvx6N3jcoNaDWnfku56dVVFAL+77bgv71xfib9Rz06RJSCo1H604zBerMyipsTOiaxhvXNWTnjEBjZ6u+4RebFq3mg69Qhk2uQuZKw+DqwpfnYrDFjv/Ts/nt6IKovVa3uwaw+VhARhVYr6ss51IWBqRfmkP3u6azqJJizCbK1jz5WtEv/sbAB++cT1dr7ubQe0G1c7K6JPSG1J6n+iQgiCci5w2WPYapN4Po/8JRXtheirMugVu+Om4uy0urWJ+3j46Zn3Icz8oRBW5ASuFASoKJkdDj48IWjSc0DGtVEXSFD/dCllrQFZD3CBAAVsluOx0smYCvTBIDt5J8za79Enayfs7FJbuK2Ji73bcdUEHEsKPn9T9L1mW6DkimnW/HcZpc7PCzwFBcNueTFaW1xCmVfNq52gmRwYdWTpFOBeIhKURLo8LteR9mozGAEbc8zLlkx9g4w/vsM5vDx/89SA+ah+WXLUEX61vK0crCEKr8bjA4wTfcG8zsfFIB86qvBPuVmh3onUc5opVToyVBp4eOYXqsBze/n4+4dPzyX5PpmpDWttOWLLWeL97XJB+ZMDByOdg6FRigIwjxdasWsp1c6xM+KEAjQyfTunH8C4nN4Q4ZXQcAAc3FuHoIANqXIrCMx0juTEqBEMbmGdGaFkiYWlEpb0Sq8sKwA/7fuDl9S+jltR0iulEZpW3p7rFZcHhaco4IkEQzlk6X0iZAouehb2/Q2maN2m5cfYJdxsd4s/Lhk6EVsDhMDXbR6ow2fbUPr5h72SGXtXG1655vhLcTig5ANOP9LcJ7VqvWP/+Q3g6fSa/7Kni0WQXw7tcetKnlGSJPhfH0+fieK496aMIZxORsDTi54M/A1BkKeLl9S+jV+kZHT8am8tGsD6YEbEjSApOIkgvJooThPPepf+Bdn0gYxXEDYb+d4LpxKNPInQafh1wAT/0Gcm1sxbz5Dcfg1qFB5jRdTSFNjOGrm3n9WXf6uWs+2UmKo0GtVbHJkVD2tCxJAbrePWPUQBUawPIWvghfiu/Q9EaUbQm0JmQ9CbGRPlxa8aLSLHPtPKVCGcbkbA0IkWJZYuUxchZIwF47FszyRm/ABB8552EXXR1a4YnCEJbIqsg5UbvVzMk+hp47oW3ebf6GdqvW4fa4+GrpN7MH+zih5tvO03BnpzMnduwmWvw7z+UR9p7m6naWasxujUsiBlHfOlO8nUhhFpy0JWVo8eJFgc67HX6DZeauiAWMhGaQyQsjZia1Y3VuWWYUvrimruEpKxjw+Yc2VmtGJkgCOcSWZZ56K2Xmbt/K/P2byYqyMOfyWOJ8Ytp7dDqsFZXERbfAcPoy+Ggd56YP/onEhUSAv2/5bIvvmdTfCI9sw+Seng3ISEhjBw5krRDh4iLCKEwM5NtuzZzb8fBrXwlwtlGJCyNCC53MvqwieALB1GwfzG+w4ZRs3w5AO5SMUGcIAgt65IuvbmkS9sdaWitriYgLJyx0eE8mp7Of1x6pu/cx8W9elLlcnPNkAvZlFNIlV80sJuSkhJmzpwJwKYjx1A7IvDxEfOhCM0jEpZGfHbIzqwed/DU9O/oCegSOuGxWrFs2IA2Pr61wxMEQThpHsXDFb9dQb45H41Kg1bWopE1aFQaNLKGnIrD/MftzwWyn3duFZWOAa6dlOmuAMBalA9B7fkEXz7Zllbn2BFODbfZRmDFgV1yscnioEwyIykqtPa20ydHOHuIhKURPydexuQaLWVJD7Aywcztd49A1mkp2LuX4ISE1g5PEAThpNU4a0irTOPyjpfT3r89To8Tp9vJrty1bC7fiwMPNY4aCOkELju4HXTQZqF2Z+JyOsncuglGtue1ztEMDfQlQK3GIEl47G40xVakgTKSRsa8eSd+c77FefggVYVmwh9/rLUvXTgLiYSlEXdV6Wt/dmmMvPXRhyQmJrJhwwb69evHpZee/LA8QRCE1lRprwRgXMdxDIwcWLv9+h1f4ZDc6BSFPhe/BfEXAKA4rEivRIBvGPkH95F0cDv/GDuaru161T2wRgW+2tpfAy4dSMClAxGEUyESlkZowiw4i7xtrU5NFdXV1WzYsAGA6urq1gxNEAThlFQ5qgDw1/rX2R6j9cdjKeSrW7aiVR1LPOzFmegBOSAKndE7UaZW9EURzhAxFWAj8uRNlAVvwqOxEtbbQ2xsLJdccgkAYWEnN0OjIAhCW3C0hsVP54dH8bA8ezl3/H4NfzqLKZeok6wA2IoPA6AJjCWoXTR6kx97Vy47w1EL5ytRw9IEbo2FirAtVOR6fy8tLUWSJFRiMS1BEM5iVXZvDUuALoC/sv/i4b8ern0sVy1jrszB6B9du618/xYCgNkffIht+je4nU5iuiWf2aCF85ZIWBrRsWNH0tLSCA0NRVEUCgsLMZlM9OnTh+Rk8Y8qCMLZq8JegVpS46P2oVNAp3qPb0/7k9RedyDJMigKYUWLqXZqieg5mNjuvYhL7k1wdGwrRC6cj0TC0ggfHx/i4+O5+eabAZgxYwYlJSUEBQXhdrtbNzhBEIRTUGmvxE/nhyRJxPnFMeeyH7lszrHZu+/a+R69t3/E1MHP08unHcbSrXDNJ0xIFjN8C2eeSFiaQFGOzW7bt29fvv/+e3799VcApk2bhk6na6XIBEEQTt7nuz7D4rLyfwsnoVdr0an03N7xAnSSGpx2DpZns7AqiydWPMH8nHykkM7Q9bLWDls4T4mEpZnat29P+/bt0Wg0HDhwoE4yIwiCcDbpYAxkV6WVjaWHcHgUnIoHpwccioLnbyv/SLKEctmbSN2vAK0YFSS0DpGwNIHH4wG8nW2//vpr7HZ77WMVFRWEh4YiiQ64giCcZZ5PTKW8Yj0DB8yr95jDZcXqrKSgZBXpB56gIMqXKL1/A0cRhDNDJCyN2LlzJwCZmZl89dVXeDwefH19qampAeDwhImUV1QAEPrQg4Tcc09rhSoIgtAsNnseen1Uve3fHvyLfTVVaCUJraxjn3Qzq/ct5KWoq1ohSkHwEglLI4xGI2azmW+++QZFUbj88stJSUlh3uuvs95sxmC11pb9efG7vOfzAQa1gXXXrUOWxDQ3giC0XTZbPv7+vepsm5O1gcdyAoFAfKnBhQqbNA6AyyvN9PM3nvlABQExcVyjgoODAXC5XMiyTO/evVm8eDHrzWY6ZWTS+cPptHv7bRb1lliW7G3ztbqsPLv6xVM6r+cEI5DyK6w8+ukmPl1+mK/WZOB0uUmvTBf9aQRBaBabLQ+9LrL293VF+3gkzU60lM++1HgODR9CxvBUfu7VEYAqlxgZKbQeUcPSiKysrNqfTSYTa9asYdWqVYwePZpBgwbVPvbgmD0c+PUbpLJ3UFQ2fkv7mZ6qyVyV2qXZ57z3m+/oOOc7UsZezvCb76zdnpFXxOh31xHt0HClWYd9UxV24OOvD/N5n2n4+fswa9wsQgwhp3TNgiCc+9xuCxaXmWKPH3GKwo/pa3ksU028XMEXPZMJ0AfUlo3We2e81UrScY4mCKefSFiaKDExkdzcXBYtWsSgQYPqJCtHvTv+Bnb8nEvQvn/jRiI+vT/0LQSNvoEjNuzr3BKCVswHYMu831HrdMhyBYP2PscY2xc40JKr8lChUghwH3vxCDfHka3dy/Afh/PFmC/oG9H31C9aEIRzVn7RYh5hOhWZgegz12PDh0GaQ3zd/yJ8/2dtIX+1d1BBhahhEVqRSFgaoVKpcLvdlJaWkpiYSI8ePYiOjm6wrCRJ9Jz0JCxXwV//9G58Odz7fdCD0PcWCOpQb79Sh4slZVXY3B4eP5BDapfeDNm4BIC/tu3kWcMnAKzSPUhB70foMWEqADvzdvPqVx9SaszhtWuf5dFlj1JsLabMVtbCz4IgCOcaqyGFCqmEy30LCZbKGBAUxrj4Cajk+m8Le8024FhNiyC0Bkk5Bzo+VFVV4e/vT2VlJX5+fi16bKfTidVqbf5x9/wOv9wBLlvd7f1ux3zRq5S6FWINOorsTpLX7K5TRG+z8MCXr9T+LiV2IYWddBx6NTEjb69TdknmEh5e9jD/TXqNLzd+zugh45iSfHPzYhUE4byzqryaSdvSWD2gKx19vLXAlU4XlS430Xot8pHmn3UVNTyyLwunorB+YBIq0SwktKDmvH+LGpZGaDQaNBpN83dMutz7BWAthwVPwbYZ2DZ9TUefG0+4a8bYQSgX/8HSzz/E4nAQnzqULknd0Wrrf7oJVgXx+aEXiNzrx794GN+SGDweJ5KkRhIvLIJwVtpXto+X1r3EJe0v4aK4iwjzafmV4bOsDiSO1ZosKKnklp3peIAonYbefj5kWx3sqLHSw9fAp93jRbIitCpRw3Im5Wxm2w/3cHGfj+tsTjLquTQ0gAnhAbWfdJrKsrOYshn7APCEOCgK+5bK6BV07/Y24eHeKbQ9Hgf5+b9gMMRiMiVRUPg7Rp+OBAUNPulLcXg87Kqx4qdW0amZMQvCqSovLyczMxNfX1/at29/zq2c/s2eb/j3xn8DEOYTxu8TfseoadnhxN1X7qTE5eYNYwAGrZrPLNVssdv5ukd7VpRXc8BsI0SrYVyoP2NC/GtrXAShJYkalrYqug+9HtvAerMVlyShliRi9dpTqgkxdPOOCCqNn0NF3BJcunIAdu1+iOqafRTkz8buKKgtr9WG4HCUoFIZuXDYjpM6565qCzftTCfP7gRgVLAfn3SLx6ASo+SF+qqqqnA4HAQFBSHLp/43sm3tZn5f+CcexTsDdXR0NDfddFODNZBnq6P90LSyliJLEX8e/pOru7TsgoP+Ng8lanjMXAFm77Zgu4cRso7RCWJGW6HtEQlLK4gzGlrsWJIsYYs/SEnnn+o9lpk5vd42h6MEgCD/0RzcWIgkS3RMCW1W0vTY/hyCNGo+7RZPtt3BXbszuX9vJp91b3/yFyKcc1wuF7Nnz2b3bm8frZCQECZPnlw7t1FzKS4PJTP2Mu/wfOI9IVxo6IljdDDfzvmBn3/+mcmTJ7dk+K2qxOr9Pw03hnNPz3sYFTmMqgUL0cbGoAoKQjYYkI1GJJUKZ6EZxa0gaVVIGhlZIyNpZFDLJ/y//jVLBo2MaVICZosLc5UN248HKd6xk+Abk9BGignihLalWQnL9OnTmT59OhkZGQB069aNZ599lrFjxwKQlpbGY489xqpVq7Db7Vx88cW8++67hIeHn/C477//Pq+//joFBQX07NmTd999l/79+5/cFZ2HOt51C/JBK3Z3DmZLGm5XDQ5nOU5nKYpSdxhiVMRkVn/VlX2VUYD3jWTg5b5k7ZpPePtODJh4NSr1if8sDltt3BUdRoq/kd6KD3eRyZ/Flafr8oSz1IYNG9i3bx+XX345fn5+zJgxg3fffZfnn3/+pI5Xsy4fy4FSHBoX7QcmoloP6p8KQO997TmXHE1YUiNT6RrUlTlz36b709/VK+d/9W14HAOOexxVgA5trInDtnxWl23Dx8+3tl+eu8BM//iehPpo0ftoCQ7xwXVnT0q/2UPR+9vwvygW3yHRSCrRFCS0Dc1KWKKjo3nttddISEhAURS++uorxo8fz9atW4mPj2f06NH07NmTpUuXAvDMM88wbtw41q1bd9yq4JkzZzJ16lQ+/PBDBgwYwFtvvcWYMWPYv38/YWEt39HsXCRJEh0631dvu6Io2B2F5OZ+T0bGewCk71+OvXIEUV2g14gezPtoFytnLsBt28DhzRtY+9N3JA65kIFXTiYoql2D5xsd7M8H2UVUu93k2BwAfN1D1K4IdRUWFhIWFkZKSgrg7cDucDiOFdj2Pax9HxzV0HksjHz2hCsBu0qs6IJ8SO6QzLItq0hTh1BONWq1mrvvvvt0X84ZVWotBeDHAz/y44EfSc4dTPGwt1AkDZ0O/UxszlIkfUBtshJ8QydkHwMepwecHjxOD5YthUhqGY/FxabsHVRIVZgC/fHz88PpdHLImU+kI5LEv51XHaQn9J6eVC3MpHJ+BpYdJQTfkIg6UPRTE1rfKXe6DQoK4vXXXycmJoaxY8dSXl5e23GmsrKSwMBAFi5cyKhRoxrcf8CAAfTr14/33vO+oXo8HmJiYnjggQd48sknmxTDWdPptpW5XGb++mkGB5Z1wuDnpkOPGsoKKsnY8heKpxS1Vo/L4R2GfeFNd9Dn0vENHsfsdvPPtHwWllTip1ZxV0wo10aeXDW/0EblbobtMwEFelwFMXVrPN1uG5mZH1FevhatLpS4uLvwM3WvU2bFtu18+9cKhiZ0IMbXyPLly+nYsSM33HAD7J8H318LiZeDfzRs+MR7non1mzGPsu4ro/TL3Wi7BbLdfpj0rAx8/Xy58LqLiYqqv4Df3zkcpWRnf4HFmonJ1J2Y6JtQqVquabYxbrcdSQJJalqftZE/jsSluLgg+gKyq7Pp8ceV6N3eJpq+cSV06aJB0umpXukLQMgt3dB3CWrwWIqi8P7LbxFqDOKaR6Z443G5eemllxidfAGDrhzR4H72rCrKvtuHpFUR/nAKkixqWoSWd0Y63brdbmbNmoXZbCY1NZW0tDQkSUKn09WW0ev1yLLMqlWrGkxYHA4HmzdvZtq0abXbZFlm1KhRrF279rjnttvt2O322t+rqqpO9jLOK2q1kYuuvZMuySXMeuFmNmcCyCCpQNJz+aMv4x9m5ItH7ubA+tXHTViMKhWvdo7m1c4NT6AnnOUOLYEZV4F/O5BkbzJx7QzoemltkT17HqOkdCkhIaMwmw+wefM1DOg/Bx8fb03bZznFPF8Bzl5D+UPxMCjzIDckJ9c2H7PvTwjrBld/DZIE276D7d+dMGExdA0iYFInqpfn0N0RTt/eifhf0h6708OmeRnUlNuJ6uRPQt/wOm+ubreFTZuvwuksx2TqRnr6W5SVraR3r2/OyNB/m72ANWuGoygOJEmFLBtQqQyojnyXVT6oVHpUKh9kWY8kGyixFvN434eZlHA1V303BJ3rRpDAGaxhwLRjnW/9xiqUfLmbki+8zbs+fcPx6RWKJtyI7Kth8Vd/sCZ9K4qkEKs/VmNaXVwJEpj8j/8GoYv1wzQ8horZh1AcbiS96PIotK5m/wXu3LmT1NRUbDYbvr6+zJ49m6SkJEJDQzEajTzxxBO88sorKIrCk08+idvtJj8/v8FjlZSU4Ha76/VxCQ8PZ9++fceN4dVXX+WFF15obujCEbFJIXQePIoDqxfjGjKKB265gYy9v1FSPJeNvxcDkLd/TytHKbSaddOhXR+4ZZ43YXkxEH64Dp739lNyuy0UFc+jS+cXiY6+HrfbyrLl3dm4aRLDLthMrs3BMwdzuTEqmFujQ5mRV8rHksz9yR3Q6480LQR3hJ2zYO8f4N8OxWlBAh756xE0Kg2TEibRP7Jurc7e1cv568uPsVZVYgoJZcxlD2FwKcx6ZRM2sxO/EAO7V+SSd6iSC687toZXaelKrNZMBg5YiNHYkayszzh46BWKiv6sHfp/Olks6SiKg06dpqFS+eB2W/C4rbjdVtweC263zbvNY8PpLKPcWo4HBY2jmCn/Gknvw+HYLW8S5qMieOiDdY4tyRJ+F0ZTfMA7OtCyqRDLpkLsOPlGvwKAdtoQeicl03VQcu1+VcXe8qag448GchaYqfwzHU20L7JIVoQ2oNl/hV26dGHbtm1UVlby008/MWXKFJYvX05SUhKzZs3innvu4Z133kGWZSZPnkxKSkqLDGX8u2nTpjF16tTa36uqqoiJiWnRc5zrxt57P1u2rqO7z+es3fC2d6MKsvd2BSS6Dh7WqvEJrUilBacV3HZvwvI/JEmLWu1HReVGwsPHUVW1HYCQkOEAZNsceIAbooLpYtRzVUQgH+cUM7uonOHBRz7R978L0lfCj95JFK2GAG4KDMJoK8PisnD7wtv5cNSHDGrnXbOroiCfee/9h4QBg+kycDCb5/7OT/98mmE3/wtzhZ3rXxqIX7CBRZ/vZveKXHqOiCYwwtuEolJ5+8VYrZn4+HTAbi8EvEP8zwSH3fshoF3UtajVvo2W35a7kIDNT5L92kr6EVq7vZvpAClj6jffaOPrJx1b1em1P9827d56r8E1Zd5aaVPo8RMW88YCZL2K0Nt7NBqzIJwJzU5YtFotnTp1AqBPnz5s3LiRt99+m48++ojRo0eTlpZGSUkJarWagIAAIiIi6NCh/vo54B3mqFKpKCwsrLO9sLCQiIiI48ag0+nqND0JzadWq5l9/VRMea8SwLH5WMa/eBGRkRMpLJqN2ZyG0dixFaMUWsXQqfDVOHijMyCBSgc3/lL7sCyr6dL5efbsfZzCwj8ACAxMJbHrywAkm3yI1Gm4a3cml4b6s7C0CpNK5qVOf+vErfWBG36G4v1gr2bKpn+SEJLEq0NfxaN46Pl1T+5afBc7p+wEoDD9EIrHw8hb7sLHPwC3y0Xe/j0UHNyAQjwuh3dOFrfL+13+28iWoKBBBAddwPYdd6BSGXG7zURHTyEwcODpfBZrORzFqFQ+TUpWAAprMgmpOjanzMArriF51FhMwQ0nWJIsEf3a0DrbJjEU98yZ7Nu3j4yMjHqvwdUV3oTFLzSgwWMqioL9cCWaGJOoXRHajFP+S/R4PHX6k4A3EQFYunQpRUVFXH755Q3uq9Vq6dOnD0uWLGHChAm1x1uyZAn333//qYYmNGJEdAS/S5Po+beEJTP7XTKz3wUgN+97Bg9ajdzAYmjCOSy6L9y7Fnb/CooHksZ7m3D+JiJiPAEB/aio2IRWF0pgwACkI7UxPiqZWb068uKhPH4pLKezUc/0pDj8Nf/zdyRJENbVu8+uALKrs6l2VGN2emcxkziWdER07IysUrHok/dIGDCYbfPnADDkmnHM/TCNmS9twOCnxVxhJ2VMLP6hPn87jYqePT+lpGQpFmsGJlN3AgPOTLICYHcUN6s2Z2n2Kg5Fm+nUZwD/Gv3mSddQT5o0iRkzZjB79mzuu+++Y81xQHVVNTrUaI6zmKGn2okz34xpmOinJrQdzXonmjZtGmPHjiU2Npbq6mq+++47li1bxoIFCwD44osvSExMJDQ0lLVr1/LQQw/xyCOP0KXLsfbkkSNHMnHixNqEZOrUqUyZMoW+ffvSv39/3nrrLcxmM7fccksLXqbQkLtjwvjxp10wqO52SdKiKA4CA1NFsnIesjndvLKihj+2J6FWyUy2uXlopILqf0aJ6PVRREQc+zBSlpdL9u4d+Pj70753P75ObrhmtSEPpjzIPYvvYcgPQ/AoHsIMYXw59svax/3DwrnsoSdY+uVHHNq4jsDIdlzz3GsEhIcw6YkADqwvoKbcTmRCALFJ9UfLSJKK0NCLmv9ktACHvRittmlTNLjddnIr9wPwfxc+f0rN6SqVivHjx/POO+/w2muvIUkSPj4+6PV6zCXeGpb5n/2KRqvB4rETEx5F94G9UQfokA1qZKOGmtV56DoFoPI9d2YRFs5ezXo3Kioq4qabbiI/Px9/f3+Sk5NZsGABF13kfSHYv38/06ZNo6ysjPj4eJ566ikeeeSROsc42mR01DXXXENxcTHPPvssBQUF9OrVi/nz5zc62Zxw6job9fjHdaeqail+fsfuiaI4CAwYSFLiv1sxOqG1/Hv+fmZuzOaOoR2wOt28s+QgKknioVEJx91nz4qlzJ/+FiigKB5C4ztwzXOvofM5/rwqf9cnvA9zJs5hVe4qNLKGC2MuxKQ11SmTMGAQnfqnUl5pZk+xlRqTDkVR0OrVdG/DNQEFhb8CUF2zH406ALXaH7W64XlNsrI+4bbgKnqN/IZAfeApn9vf359BgwaxcuVKFEXB5XJRVVWFR3KjUmR2Zu/FjLeGfHP6DoKWO/AbEo3/xe0JubkbJV/tpmj6dkJuSkITLma+FVqXWPxQYPuOdygpebv2d4OhPX37/IRWG1C7TVE8OBzeT4piFehz20X/XU7/9kG8PNHb2TL+yT8ByHjNO6zZnlGJI7sadbABfdcgQOGDO28gPrk3Y+55mNLsTL6d9jCx3Xty1TMvt2hsyw8Uc/+MLVTbXQCM6RbOe9eloGnqOlaKAvZq0Jm8TVJ/ZymDzNWg9YX4IaA6iVXaG7Bk6Yn6gR2dPl+FJEl4PA6Cg4fRq+dnLXJu8PZHmTFjBiUlJTz88MN1Hlu3bh3z589n8KDBDOo9APfuSqoWZqCJ8sVveCwqPy1F729DHWYgYmrfFotJEI4Six8KzdIz+UHy82PYvecxFEXCak1n2/ab0WqDsNnycDhKcLuteDw2fNUJxCiTiBp2e2uHLZwmXSJMLN5byOCOevKL1gLB9InxroZcMfcwNStykTQyitODrnMgQTd2xWGxEBITh1qjoSY4jGUDx2DzMaEuLGdCWACSJFFcXMyKFSsoLy8nOjqaYcOGYTA0ffI2RVF44qcd9IoN4PnLu7E7r4oHv9/Kc7/v5p8TurO0tIpl5dX4qGQG6A0oFQ6igwx0jTjyIrjnd5j3OFTnQ0AcXP4udDgyGi5zDXx3LdiPLDER3h2m/AE+DU/G1hxqtT9+fj3x8+uB21WDy23B7bZ4/6fcVtweGx6PjZqavQBERV51yuf8u/3795OZmdng5Hrbt28nKSmJi0YfaS670A9dvB8Vcw5T+s2xqQ0M3c/MiCpBOBGRsAgAREZOxGjsyMZNE5EkLbKsRUJFYGAqOm0okqSjcvFcNK/voZL/4LlsH9FvvNHaYQunwTOXJXH7V2u597sDQDDJYYe5JeFDMnfnoVqRgN+YOEwXxmDbX07pl7up+SuHHiMuYvWP37L98GFe6X4hcpfetA8I4J49meypsfJQuB+fffYZPj4+REdHs3XrVnJzc7n11lubXGNnd3koqLJx34hOdAz1JS7IhweB79Zncbi9gWXlNQDIuWY+2l2BdKTu+Ire7XhjdBDyT7dC5zHQbSJs/BS+vhym7gW/KJj3BIQlwqTPoboAPh0BX14G9645pefS43HgclUSHnYpUVGTsFQXs2TGC7Tr3JMu/cZjNIXh8TjYv/85amr2kdxjeov2tZk9ezbbt2+nS5cuXHnllfUet9lsmEx1m9508f6E3dcLd6kNR74ZlZ8Wbayp3r6CcKaJhEWo5eeXTLekN9m95xECAvrTqeNjAFTuW0P284+i2VZRW9Z+gon9Tpeqqip8fHxQN7I4o3Bqwv30vH3pLjYf+IV+fb4lLuQS1m+YS+bhT+nAv1CH+CBJEppQb+2Iq9TGsCl3ka6P4acyJ7JFYVGvKDq278CYTft5N6uI4RUF2Gw2HnzwQXx8fFiyZAkrV67k0KFDJCQcv2/M3+k1KkYlhvHvefvYn1fEvkIzsgRPXt+LZ4u9c538p1M7nl28EUekAWcnPx4y+vPRvAO0V7J5wOOES94Av0jvXDNZa2HrtzDscagphOSrvbP7miK9JyzafcrP5dHV0bU6bw3FlkXfcuCvDA78lcGambOY/MJbbNszDoAunV9s0WRl0aJFbN/unSPnmmuuqdeBd9euXZSXl9O+ff11wCRJQh1iQB1y5pYvEITGiFd+oY6IiMux2fJIO/w64YEXk//Ba7h+2IzKVbdc1Gv/OmMxbdiwgblz59b+PmjQIFJSUmqHzwungQRhPhVE+euO/Crh9ClE28mPspn7MK/3x5lXgypIj+/4Dkz5chNr0lRIkgolr4yf/YJ5LF7B5vFWc5h8vR02Dx48SOfOnSkqKgK8a5E1x+tXdubpH79myW4f/LTVTBt8GB//JPDmK3RSa3G7FdyhBjCoadc+AIA/cgw8IGtg7mPQ/QrY8Kl3h17Xe78nXw1rP4CKbG/yggQ3/XrST99Rdoc3MJ02jIMbV7F+5hIAfMMlagq1fPnQ47S/2JekQROIjr7+lM/3d5s2bQJg2LBh/L2rYnFxMWvXrmXr1q107969zihOQWjLRKdboR6Ho4x13/Qn4Gs16mIJ1aReeH7cBoAiy4Q+9yyh11xzWmMoKyvjnXfeqbe9Xbt2lJWVYbVaiY+P55prrmlWPwihaWz2AjZuHI/bbUOrCcJqy6Jjh38QG3UHNWvycGZXowoxYBrSjnnpJdz/3Va+ua0/CdH+XPDRahwFFrTDo6jSSrzaOZopkUH8/PPP7N7trbWQJImLL76YAQMGNCuutLQ3yM75ii6dX0ClNrJz573YNXE8Lr1NmdPt/QS2vhjJ7CQoxkS4RSGrzMKiqcNol7cQ5v7Dm5D4x8Ll70BH7+y8uF2w7gPYPxe0RhhwNyScem1HcfFiduy8iyGD17Hhtx/Z8PM8uoxqx2V3fMQH947GWuodLnzF/z1N+54tOzeMx+Nh2bJlrFy5kqCgIBITEyksLOTgwYP4+voyaNAgBgwYgEqlatHzCkJziE63winRaAIJ/FKNR68Q9cOHKNpwCn68AoC/usTgmfczg3z1pFxy+WkbMdRQsgJw4403olKp2LJlC/PmzaOkpEQsy3Aa6HUR9O83h/z8WTic5QQHDSU4+AIA/C6s+3xXWp2oZYluUf4EGbRMjAthZkEWVwT4cXViBCl+3tqVSZMmkZqaSnl5Oe3atWt27QpATc1+/P37EBl5Re02nTOTeYO68Hp6PmvKzagGRRB42IynwkF0oIHXJ/WkXYABAsZD13HejrX6gLqjhFRqGPyg96sF2R1FgIxWG8SQq+5l57LfKUorAOCe9xeQvW89f7zxFmkbN7d4wiLLMiNGjKBLly6sX7+e7du3YzQamTBhAt27dxdNq8JZR/zFCvXYduxAXSwR9uQ0ApIvZHPfPvgAlT2SuPmTT1n3yw8s+/oTsnZtI6H/ILoOHoZa23ITS5WVlR33sYKCAuLj40lISGDevHnU1NS02HmFunS6UOLj72203OikCP6z8ACXvrOSpEg/Vh4qYVRiGK8NqDucV5IkoqOjiY4++TlTAoMGcfDgKxw8+AqqI1Pdh4VdSoxeyzuJcccKXnCcA8gyGE59fpOmysyYDniQJG8tRtehfdn663bKi9IIDOtIbOJAQuM6YK6oOG0xtGvXjiuuuKLxgoLQxomERajHeWR1bZXJ+4YgDx6EZ8Fi/HfuYc89dzPy+5nE9ujFyhlfsODDt9Gb/OjUt3lV+ydSXl5+3MeOTihoNHo/te/fv5/ExMQWO7fQfKEmHbPvHcTHKw6TV2HloZEJ3DakfkfOlhATfRNOZzm5ud/j8TiIirqGzgnPnJZztYTwjFwMmZVM29eBfVotphoNccYo5j79ABq1DJJCdonEkCsntXaogtDmiYRFqMc40Fs1LR9JCoJHjqSoqAjj1h34bt2B22YjoV8qtpoaFn70DlGdu9Y7RnFWNaYgPXrfxiffytxdyt7VeRhjXBwq2EZmZmaD5f7xj3/U9lcpyPWOvjCbzSd1jULDFEUhd9oqAPTdgpFkCSRAktBGm/AdEtVgM2BcsLF2ormT8VtROf86XECezU4fcyXPVRaQNHok6v/pWC1JKjp2mErHDlOPc6S63JWVVP7+B+7yMoyDB+PTp0/jO+Vshuz1EBALnS/2Nhc1QWWxhd0r87CbncR1D6F9rxDaSX24OmIPNWoNg2U96yQr+wcXYEzrj03rXQwyQnOAbv16NukcgnA+EwmLUI/s74/i40Puw4+Qi3dphaOTclcFB6A6sojaht+WotG3Q6UxoigKeQcqyNxdSnFWNTn7vLUkgyd1Ir5HMOk7SvG4PVQUWojsGEBMUhC+gTqcNjcLP9mF1VlDWe4mAvyDGD58OP379z9uZ9rCnFK++PoTAEKUzlSX2TAFNTzVudA8f09G3FUOZL3KO92+y0Pl9mI8Fif+Y+Jb9Jw7qi3cvTuTUTgZ/etPzBo+lqtMkfx62Tg6fDcDXQOrvafvKOHQpkJklUTi4CiiOgXUK+MqLSX9qqtwF5cg+/tT8sF0wp58guCbbz5+MMv/DX+9DGo9uGwQNwRunA3qEzd5VhRamPXqRlQaGR9/HXtW5zPg8g4Y8jeREeLLF+r2LD7UjZ6ylYUxC1neLocBxd6EZeSE8fi2792cp0wQzksiYRHqkSSJvdGhJB04VtNhTu1PzC23kniBd2ZQu9VORf4+1Pr+/P7WVtwuD6W5ZrQGNaYgPTGJgZhCDKz+6RAZu0rJ3VeORq9CliX2rSsABTR6FU6bG4ABV8Uyb+VGeiX2Z9iwhpuXLDVW1v61lZ07vatLR2t6kb3Fyjdr1tIpJZReF8USFidGiZ2q8Ef7UPzhdiRZIuTm7kgqbxJTvTybynkZqEMMGPuc2lpfziILNWvy8FQ7WBqvRS/LPPfe62h8jdR0jufbwgr2+gfDJZeSuG9vnX33rM7jr2/2ERZnwunwsH/dFi57oCexScF1ylX89DOeiko6LpiPOjKS9AkTKXrtXwRecw1yQ8mwtRyWvQZDHoERz0LmKvhqHCz4P7j0xJMk7l2Th0qr4oYXBqI1qPnmmbWs//0wk6KqkIONzCo5DFJn8ox5AERaIunYsSMXXXQRERERp/RcCsL5QiQsQoPG//wHf335ETsWz8cYEEhofDyW7MOUrfQQHBPHwY2HACddBg2kOMdFWKyJwZMSiO4aWOdTenm+mdx95ZiC9dz4z1QkScJS5SD3QDkl2dUUZ1XTfVg05ZZCAHQ6Xb1YrBYbf81dx+Yd63DLNgDiQhK55f4JOGwu9q7JZ8fSbGa9uomohAB6jYohvkeItzlDaDZNqA+BV3Wh9Mvd2A9XYNtbhrvKjrvaCUD5rAPoEwJQ+dW/V03hKrVS9P42ZL0adbCe6PUVWPv48HnvVPoqDtZXWwGIL8hpcP+dy3LomBLKmDu6gwIf3PsXf7yznfs+HFGnnOJyglqNZDAgSRKS5kjz5PFmcnDaQHFDSGdv59yQzt7tJfsbvSZZJeNxeXDY3Gh0KjxuDwCBbjejzRbm+hnB3zuX0NDAofzn6v+I4fiC0EwiYREapNZouOiO++nUL5XcfXsozkpn/9qVbPz952OFJF9U2jAGXB5MXLdgdD71+6tcck8ynz26Ep2PujaR8fHTktA3nIS+4VSX2agoslCRZgFg+erFpI7oVbt/TkYB3371HTZPFTqVL5Mm3IR/gC9h7byfprV6NT1HxNDjwmjStxWzbXEWc6fvxD/MQK+RMXRNjUStFfNMHE9+/s+Ula/F5JuIgoK55gA15n1QpMcvZCi++9tRs8ZbK2DoFYoxPAJJr0I2nvzCgJZtxSBJhE9NQdapGfrRdm5Ns/H1wAv5XJIIKSrm/Z+/weDxEPfdjHr7a/VqzBV2nHY3LofnuOcJuOIKyr+dweGLx6IOC8N+8CAh996DfLwVpP0iocul8MdDsGMmFO4G3wi46qtGr6n7Be3Ytzafb55Zg0anwm520e+qKPrkHBkCrihcFDua+3vfT4eA+k1cgiA0TkwcJzSL3WKmNCeLvWv2kL7dhW9we0pzjg0tHjypE7FJwQRGeqdvd1hdfPLICroMiGDklETK8s1k7y2jML2KgsOV1JR7l7YvDdmAR+2tPenWrRv9+g7g+w//wG4oRvZouOLya+jet1OTYiw4XMm2RVkc3laMwaSlz9h4ug2JQqVp4oq+54nDh98iPeNdDIZ47PY8FAW0mkD0hmgqKzcD0E3zCa4/NUh6Fe2eHwSAy+lElmXkIxOOKYoHSZJRFKVJ8/JUr8qlcn46Yff1RhNmoOi9bTjzzRifGUD25g0ELFqAVqMlYNKV6JOS6u2fd7CcP97ZjsejoCig81FzxWMpBEYY65V1FhRQPnMm7rJyjIMHYbroohPH6LTBho+PdLqNg9R7wb9pw7BtZicHNxZitziJSQpGDrMzYpa31ueTiz5hYFTLzrMiCOeC5rx/i4RFOGXVZTZ2Lc9hy4Ks2m1agxq9UY3N4sJh8c7rf7TPikojExZnIqK9PxEd/QluZ0SlkcnMScdsqWbRokU4nd7mB5XLwKj+E4nqGExQpBHfQF2TJ6urLLaw6c8MKmf/QuL+GQTfcTuu8nJQFGSDD/4TJmDo3q3ln5A2SnF5cGRVoYkwIhnUrPxzIE59Of0HzEaW9Wg0gdhsORgMsWxYeBU2w2FUdj/ar36FsMn9MXQLxmG18NE9N+OwWtDo9AR2cBE5dBeSpLCjOInp22+lZ9gBHujzE7KsQ5LUuFzV6HShaDSBaLWhdI57norP0qksKkOtUqN1qQmcmICxf9P7clSX2cjYUYKskujQOxSDb8vNA9RSftv7B3NmrcMlObm8/xjGXzQCWTRTCkIdImERWo3T7qbgcCVFmVU4rC6qy+wc3FhIXPdgIjv5ExbvR2RHf9Sa4zfTOJ1OFi9eTPr+HIw17bEVqWur/g0mDR1TwkjoG87KHw9Qkl3Dne8MQ9NAs485u4CyTXvInr+ewOVfA6CJjUUVGMCGikIqQoMxtGuHzscHnY8RncEHrY/R+/ORbVqDAZVWi1qtQZWtoNrhQhPiU7swnCbEgCpQD7Lk7RuhAIqCpFeDr4acAxVIEsQdHSLcShx5NRS9s7XedgUPB0bdAbK7zvYgRlHGYpxuNdH7r6f7/c9iralmzpuvkbVre2250ORS2qUW1dn3gaWv8eGYN1AUOy5Xdb1ztu/2Ga9um8G6gnVo0HBl7ASeGPZ/qOVzo4W6otDC5nkZFJaWUn7QiUVThY/Tj8BII2Pu6EZwlG9rhygIbYZIWIRziuJRqC6zUZZvJnd/OQc2FmKpdNQ+3nVQJCNvOjZ5XNYvSyj97xtoS7ORlWNvxAHvf0nkSO8IpPcmXYJ/UAiR/Qdit5hxWC3YLWbsFu93h8WC3Wqp00EzJWgUcf7dCegVjbPEiqvYimKv+0b/dw6NzLxib5NXeHs/Jj6agkp98s1SHpsLj92N/XAlnhon7nIb7hoHskGNJtKIoUcoquP0LSl8azPOAm8/IeOACFDAnJYDpWroZoZhpZSUL6G0dHntPtuKuvPutjt5c3QC4y/syJuTx9c7bucrDuMTaq+z7faFb/HhqEfRqGQUxeldEPFv92G2ZyR7yg7xQO8HKLGW8M7Wd7gp6Sb+0e8fJ/3cNGRRSSW/FJajkiSuiQhiaJCpRY9/PFsXZrHu1zRC40wYfDWMvbsHxVk1LPl6L7JK4pqn+p22JS0E4Wwj1hISzimSLOEXYsAvxEB8jxD6XdaejB0lLPp8DwAJfcLqlC/68TeMJRnYL7oOyeiL9tePccsaQoemAOBxOnHIEp06dCb19uNPPa94PDgddlwOB26Xk7T/LMGGmaBrvRPlKYqCx+zEfaQfztEJ1twON6Uf7aDK6qLHsHb4h/mwatZBNs/PpP9lJz8DbMEbm/DUOGt/V4cZUJm0uIqtmDcWUrUkm6CrO6PrFFDvDTHswRSqV+RQNT8D88YCwu7pReAVCVSvzqXyj8NIh/yI6HQbLgUq2i2npsSbrAB8tqOARxYe5IG/HW/KG+/j4x/AgU1z0ARlkZB0F/vyq7jty78Y2m4tatmNongYMfwQkiTh8TixWrPweOy8NPderki4gokJEwF4Z+s7fL3n68YTFpfduzhhTTHED4Hw+v1bjppVUMYDe7PoaTLgUhSu2p7GVz3aMybEv3lP+kmwVDswBeuZ9ETf2m3h7f3oOzaORZ/vIWtPGXHdgk9wBEEQGiISFuGso9Wr6dw/ArdLYenXezmwsZDoxCBkWSJ3/ho0+zdhDYgm5V3vlO2l116K2teIWuutfajJyUaRJHxCQ094HkmW0eoNaPUGFEVB49SB6VgNiSRJqHy1qP6n/8S6z3cTDRgkibD2fkQNjGTTvAw2zkmnOKuaikILOh81oTEmsvaUEtHBn6TBUbTrcuI1bjwWJ8bUSAyJweg6+CP9rbbGXWWn7If9lHy2C02UEW2cHyo/HcZ+4ah8td6k78IYDN2CKXp/GxV/pBF6d09Mg9uhWF3UbCjAtdtBBLew03SYF7beWXvsXQXeZp3fwy+hb8UWxk2aQEiMd92eXiOury2XHBfES/0qWbtezcoVNxIbm1GbOMmyBqPRu7ZQ77De/HLwFyJ9IymxeGcsTq28mG+e8c6n039ch/o1UfZq+GIsFOwElRbcTrjsTeh7S4PP1Ve5JYwO9uOrHt4EMXLZdqbsTKdgeK8TPsctwVrlwMevfp8at8vbrDnn3fpDsAVBaJxIWISzVuKgSDb8cZj96wrIWbyFlO1vonI7cAXGEPOf12vLBffqXGc/c7a3c7BvRFSTzuNxudj71Vz85WCs8cdvAjqqw/BoMneVEKmV8fx6iEK9ikvu7sH6P9KxVjsIjTVhrrCTuasUtVamIL2KAxsKSRwUiY+fFo9bobzQQlleDRqdmqAoIyNu7IpHUdCEG9F3rp/YqPx0hNzWA9veUsxbirCnVeAqs1O1JNPbofXIRG+aUB+Cb0yi5NOd1KzKxXRBNH6j4jCNjOXwS7PQWSLpFN6ZML2DYN8A9pZYas8xafxF3HHBXehP0P8o0uzEJzsNlaWassM69ieuokvqkDplnh74NE+tfooX176IRtKQnHchV4TdhE+Yji0Ls3A5PAy9pu49Y9v3ULwf7loBYUnw6SiY8zB0vxL09auRTWoVBXYnVS43zjPc6G2pdmBoIGFJ6BfO0q/3ndlgBOEcIhIW4aw2+bkBfPLwCnrs/gSN0/vm2vWbDzF2iD3uPtV5uQD4NrJqsKIo7P7qM1bNmc3Q2Nsol3PoceNkwNufxFVsxVVuQ1JJqIMNqMN8kGSJsPb+hP13GIfnHEa7Kpey39NIfDaVCY80PP264lGY8dw69q7Jr7Pdx19LVUkNpbk1HNzonVhveFY1SQMjGzyOpJIwdA/B0N27/o7H4qRizmHKfzqAZXMh+i6BGAdGoe8YgHFQFJVz0zFvKSTi4T6Y1+Wjs0TiHpzH0EEfs2HQCZ+a4z5fO5asRXaAIzQS2WJmzluv4eu+jXZDJtaWCzYE8+GoD7G6rCz9bB92jYeLb/OuQ7RzWQ47/sqpn7DYq7zT5Qe2B5Xm2IrLrrr9Z476R3wEV29PI2n1LhQFAtUqfundtGHxp8pS5SCyQ/2mJ7VGxYXXd2HZjP1YjlMLIwjC8YmERTirafVqLrk3mf0x/0e7RW+jLs0m55pJhE+bRsAVExvcx1zoffM3RR8/qSlYv44l77xOgctOZ98O+Gu9zUdlP+3HkV2Dq9BSbx9VsB7f1Ch8B0YiqWXaX9qe3FW5aGqceDweZLnhDreSLHHDS6nYapzIagmNToXT7karV+N0uMnZV86qHw9QVWKjMr2yyc+N7KMh8MoENFG+2NMrqVyYSeWCTPAcq3JwFVgom3UAy7YifAdFETBuaJOP/7+s5SUUlTvoEVXNTv+OxAZVkbMPfnrvYx7KngmTf6hT3qA2EBTmx669OWTsLGlw4sFaPSbB6rdh+iAI6gAZKyFlCvg23KyX4m9k9YBEFpZWokJidIg/Idoz83JnrWq4hgXAYXUjqyVkleh0KwjNJRIW4azXPjmE9slj4KkxuEpLybxpCvn/939UzJpF/Pff1StvLilGUhQMgQ33GVl4/53sLPbO7pqSUUA7owOOTBFiP1iGvnMIpgui0UQYUQfqUDwKznwzli1FVM49jHljAcHXdUUTbqQqxIBfifW4ycrf/X1la63e+6+p0aqOXF8IH9yzFFeBGXtWFbrYpo2Gk1QypiHtMA1ph7PYgnV3KVXzM+qUsWwuxDggAv9LT75DMIBe5cRXbaeiwsM16pnkWf3IIZ6hYRlY95ey8P1nOOwIwmg0csEFF9C1a1dSLo4jP62CP9/3rg/lF6Jn3AO96h88MB7uWArrPoCaIhj7b+jTcP+VAwcOsG7dOux2O126dGHw4MGoVGdmtmPFo2CtduBjqp98KYrCrhU5dOoThv4UZgoWhPOVSFiEc4o6OJgOv//Gvm7dsW7dSsVPPxEwaVKdMtbKCrRISA0kEQ6rpTZZGXgwl3ZdkzD06IGhrw1D7xQ0IQ2P7lAlaNEnBOJ7QTRlP+yj6L1tuEINyIUWqoCizCr8gg3ojOqTH9IqSaj8tJR+u5fQW7ujaWBm1xPvLmE/VAEqCdwKSKBLCCBwYgLqwFNf7Vr2i+TSdvuYk9uVWVnJSLJEvwv60HvITcyY/Sc5JR5SUrtRWFjIDz/8wO233050dDQTH02hLM+My+khJMYXleo4yV1Igrej7QkcPnyY7777jri4OIKCgli2bBlms5mxY8c2Gn9Z2Wpy837A43EQHnYp4eHjau9VTU0N3333HR6PB41Gg1arRaPR1PtyW9QoCiz//gDb8qvQ6VXo9Gr0OjUGg5qqEhudBorFDgXhZIiERTjnSCoVAddeQ8UPM8l/+pn6CUtNNTq54U/c62f/iEar45ILRtPxhzubnVxoI42E3deLmtW5lGwqxOJR2GvzUPHqJgBktYSPSesdph2sJzDSSHTXwCavMu07uB2qvaUUTd+Oyk9L0NVd0MY0Pr+II6+Gks92IRvU+F/SHl28P5pIY8tOZidJRP9zP3d8ezUVeZn43LcUQ2Aorpk3c4j2XDryQvoNuRCPx8OLL77Ip59+yvPPP48kSQS3a5nJ1Hbt2kVISAg333wzkiRx8OBB1q9f32jCUlq6nG3bb8NkSkKl8mX3nkdwOsuJiZkCQEFBAXl5eXTq1Amj0YjT6cTpdGK1Wmt/djqdVJVaCcY710/lsoIGz7VwZRapl4n1hAShuUTCIpyTjAMGUPHDTAByH/sHAZMmoQ4OQhMdjdVhQ69uuEr+8JaNdBl8AZ3uuOukzy1rVfgNj8VveCxul4eEKgfWagdVJTYsVQ7MlXaqS6yUFVg4tLWYtbPTCIszkTw8moR+4cjHq2FQFGS9mtA7kymbuR/bvjKK3t+G/yXt8R3a7oTJVeW8dGQfNaF39zzu5HItQuuD6rI3CP5gABRuhMBLULkd+FPJ7t27iO7YhYIC7xv5gAEDmn14q8vKjL0z2FO6h1hTLDcm3Uiw4Vitl5+fH1VVVWRlZeHn54fL5WrScXPzfsTPL5m+fX5CkmSWLO3IgYMv1iYsZrMZgKuvvhqt9vidZWcv38z2v/7gqpvuoH1UGGazC7PFidnixGJ18tvmHH49WMQdFVaiAsRqzYLQHCJhEc5JfmPH4szNpeTDj6iaM4eqOXMAkAwGcjpHoZVVuF0uVOpj/wLminJKsjPpO+6KFotDpZYxBekxBekbrEXxuD1k7S5j5/IcFn+5lw1/ZjDg8vZ07nf8ZgNJryLwigSse0qp+PUQlXPTUYcYMCQ13FzlKrViT6sgYHyn05usHBXahauT/8Oa3DD2++/CeOBPJhDNjxU38tFHHwHQvXt3Ro8e3azDKorCA0seYFvxNnqF9eLHAz+yMHMhP437CR+NdwXmAQMGcODAAb744gsA9Ho9N954Y6PH1qj9qKrajt1egEpVv6nNbDbXNgWdSEm5t1N0XGQIPnoNPnoNocHHEpOuXYOZ9/oy/rvoAG9c1bPJ1y4IgkhYhHNY8O23E3z77birq3GXluIqK6N64SLYuhKHx82Hd95Ax74D6NQvFZVGw+Y/f0Vv9KVDn/5nLEZZJROfHEJ8cgjFWdVsmJPOos/24LC66X5Bu3rlHdnV5E5bdWx/HzW+g9uh6xjQ4PHdNQ5Kv9mDyl+HT++wBsu0NA+wItA7y2vXPVb2DH2C+HXv8kiHwxQMeBqj0UhwcPNnek2vTGd9wXreGv4WI2NHcrjyMON/Hc8r61/hn0P+CYDBYOD2228nKysLu91ObGwsBkPjNRlx0XdSnDOP1WuGggIq2UCv3l/WPm42m/H1bbzZqrKqGgcqfA26Bh836TXcNrQ97yw5yD8ndD/hnDaCINQlEhbhnKcymVCZTGjj4/FJSWGq8jhFGYc5tGENB9avYffyJQD4BgYx5p6HMfiemTVn/ldorIlL701m4We72b4ku07CoigKigKWTYWg+1uTkSzhsTixH6pA8XhwVzpQbC6QJDxWF+ZNhUgyhN6VjNzAApGng/y3pimnrKG/czB7nf9Cu3sm2f0fZXDw8YeTn4hB7U08sqqyUBSFrCrvBIDRprrz6ciyTHx8fLOOXfHyxwQvcqJc3hVnaSH63TLG72JqH6+pqcFobLyTc01NDS654WTlqFGJ4fx7/n42ZZQzJCGkWXEKwvlMJCzCeUeSJMLbdyS8fUcGX3MjFYXePhX+YeFtYlG66K6BHNxYSHWZDVOQHkVR2LkkGwCtBKF3J6OJ8sWRUYV1TymWHSXUrPaObJI0MpJe5V01WpIwpoRhGhFTb/mAM8kha5nb+Wa2OLXMyqxhe9zJHSfSN5IbEm/gv5v/y/Tt07G6rAxpN4Q7etxxSvG5a8xU/vYbkdOmEXTTjbgrKjgwMJWsm2+h47y5AFgsliYlLHaLGUV94hqdSqt3PSi95uQXwhSE85FIWITzXkB42xpm2qFnKBvnpPPTa5vomhpJZbGFtC3FJA2JIuWaBNRHmhH0nQPRdw4k4PKOeMxOJJWEZDiFYdMt6P3EWO7bm0WIRsWu4alAKt/tOExhaRX/dyCHVzqfeJbh43m83+OMiB1R2+n2gugLUB1nxFdTSVoNkk6HIyMDxe3Gke1NDnUJCbVlzGYzERH1/07S9+ewZ8dBFI+C3qBFV5NHjjuIzk/P47r+sUzuH0vHUCNqleydOTmvihf+2E1MkIGU2BOvHSUIQl2SoihneKWNltec5akF4WxQU25n/e9pZO4qRZYlBk3qdMKOuG2NW1EYtmEfJpWKr5PbE6rV8EZ6AW9kFJDi58Os7h1YebCExXsL2ZVbiU4tY9Cq8NGqvd81KvwNGu4d3okg4+mvHSr7+msKX3kVSadDsdvRd+9O3DdfIx/p/3L/6//FotMzdtQoDGoVBrUaH7WaWd/NQPZ40LpdaN3eEUlrnXHsdx/rL6SSJUx6NdU2F26PQlywD+9O7k1ydMBpvy5BaOua8/4tEhZBEE6LLVVmbthxGEWB/4uPIF6l5trFe5CLrOjKHbjcCglhvvRvH4RHUbA43FgcbqwON5VWJztzK/n4xj6M7nZmEjXbnj1YNm5EHR6OacQIpCMjghRFIXbxJpzHGQp/1ONry7n+5mTCo7wdiistTvbkV3GoqJpquwuTXkN0oIGhnUJQH2/ouiCcZ0TCIghCm1DscPLcoTzmfr2rdpvWpOHW1Hgm92xHXLC3X8iuVb9jdzjR6I0EBAShhHdj2OvL+O72AQzq1LodUyucLrqu2sXzMcEMNGqxOF1YXC5sLhf5OTU8I3lfQrOH9EAjRv0IQrM05/1b9GERBOG0CdVq+CApjmeHW/j6r8MAOKqd/Lg6k2+WHUYB4jw5zFM/WrvPuHaRKJZYZNUt+Opb/yWqyOFt6ukVEkivgP8Z2twZvt2wjwH+RpGsCMJp1vqvBoIgnPNeHJPIi2MSqbQ4+Wt/ETnllto5SDz2KJxrdChhSRy0FZOj1jK+fBB9NfuZvdBI8q2Xt2rsRQ7vqJ4wbcMLGpY7XQRpxEupIJxuzWpInT59OsnJyfj5+eHn50dqairz5s2rfbygoIAbb7yRiIgIjEYjKSkp/Pzzzyc85tG1RP7+1bVr15O7GkEQ2jR/Hw0Terfj/hEJ3D60A7cP7cCdo5LRPFuE9u5ldHt4Ny/1+T8AzG6Jx7Jv4vu3LsXjdrZazMVHaljCtPWTkjy7kyKHi14mnzMdliCcd5r1sSA6OprXXnuNhIQEFEXhq6++Yvz48WzdupVu3bpx0003UVFRwe+//05ISAjfffcdV199NZs2baJ3797HPW63bt1YvHjxsaDU4tOKIJyvLku+isuSr2LWrz8z45sohu4+zB++jzD+9vdaJZ4ihxODLGP8n46yu/YXMye9mAA19PVv3srZgiA0X7NqWMaNG8cll1xCQkICnTt35uWXX8bX15d169YBsGbNGh544AH69+9Phw4dePrppwkICGDz5s0nPK5arSYiIqL2KyREzP4oCOe7qyZcSUKlt9nIN20h+/Y/2ypxfJFbgtXj4Y2MAn4vqmBHSRm7Dh0m4It93LCslH+XqwlpoPZFEISWddJj69xuNz/88ANms5nU1FQABg0axMyZMykrK8Pj8fDDDz9gs9m48MILT3isgwcPEhUVRYcOHbj++uvJyso6YXm73U5VVVWdL0EQzj0XLNiA7cu+VF/kw+O5EczPz8BzBgY22i1ODmws4NDmIjSHajBaPXyQVcT/rVjHovtuYv0L/6wtm5Jbc9rjEQThJDrd7ty5k9TUVGw2G76+vsyePZukpCQAfvzxR6655hqCg4NRq9X4+Pgwe/ZsOnXqdNzjDRgwgC+//JIuXbqQn5/PCy+8wNChQ9m1axcmU8Nrurz66qu88MILzQ1dEISzjEploPfAb/hy7SdssPVjw74KVPsryBnW87TO6Lv4y71k7CgB4FogoW8YF93WjY3LilgJ6Pr1xJJvwUfxwao1n7Y4BEE4ptk1LF26dGHbtm2sX7+ee+65hylTprBnzx4AnnnmGSoqKli8eDGbNm1i6tSpXH311ezcufO4xxs7dixXXXUVycnJjBkzhrlz51JRUcGPP/543H2mTZtGZWVl7Vf2kam0BUE4N7018Hb+2dHbVOxxK3xwz1/89NYWbObT0xn3aLJyzdP98A3SodGpkCSJqIhIAPr1S6FqqI1ZGW8QfnXyaYlBEIS6ml3DotVqa2tM+vTpw8aNG3n77bd5/PHHee+999i1axfdunUDoGfPnqxcuZL333+fDz/8sEnHDwgIoHPnzhw6dOi4ZXQ6HTrdiVdEFQTh3CFJErfHRnNtVCQPrTgAQOG+Cj57dCWpEzuSMuYkV1RsgK3mWBJkCjaAAj7+3tcbj9sDQGBkO36b/i7GiCj8QkJb7NyCIBzfKfcU83g82O12LBYL4F3a/e9UKhUej6fJx6upqSEtLY0bb7zxVEMTBOEc46tW8dmIRBiRSNqWIuZ/vIu1s9MIizMR3TXopI9b+ccfVC9dinHAAPY5u9Rudzs9WCod+PhpyTuwl8Wfvo+sUjHv2y9R2S3oInu2xGUJgtAEzWoSmjZtGitWrCAjI4OdO3cybdo0li1bxvXXX0/Xrl3p1KkTd911Fxs2bCAtLY3//Oc/LFq0iAkTJtQeY+TIkbz33rHhiY899hjLly8nIyODNWvWMHHiRFQqFZMnT26xixQE4dzTMSWM4Td452z67a1tFByuPKnjOPPyyPvH41TPm0/B8y+wZXU5AGq3DWdlDR6PQnnlHL5/5h+U5+ficbsp2bkFBbjy9rtb6nIEQWhEs2pYioqKuOmmm8jPz8ff35/k5GQWLFjARRddBMDcuXN58sknGTduHDU1NXTq1ImvvvqKSy65pPYYaWlplJSU1P6ek5PD5MmTKS0tJTQ0lCFDhrBu3TpCQ0U1qyAIJxbe4djaIz//ezN3vTsMdTOnyFcC/Y/98vR7sMr7o9nyO58+9gEqbTcqrVsI7GTD4gig0tYOJJkr7r6fwODglrgMQRCaQCx+KAjCWUvxKHxw71+1v6de0ZGU0cfvz/LavH2U1ti5uHsErP+Ih6VZADwy203qPoUKvw5sSfGua2Qr/2/tfl2vzcTflsDi/ckE+QRw4x1TCAwMPE1XJQjnD7H4oSAI5wVJrju02aVtuJW7snIbWdmf8eHyiwGYtTmHDP1/GBweymofA/P7yIQFRTPshkfp338AstGHw1ujcDsriEiIwRQYhSRJDDntVyQIwvGIhEUQhLNavxf78PM724ktcTGlqgj1ilz+ZfqZGI2dkpIleDy2v5X2Jiz92wexMvRVXg9WMA2+HdT1Rx12TBFrmglCWyISFkEQzmr9w/yJenogN+9Mp7zGCm4VC8vdjOPPOuViYm5l59Od0Rs6olHJQGrrBCwIwkkRfVgEQThnuBUFi8uJ2lWAoijodKGoVGIlZUFoq0QfFkEQzksqScKk0YImtrVDEQShhZ304oeCIAiCIAhnikhYBEEQBEFo80TCIgiCIAhCmycSFkEQBEEQ2jyRsAiCIAiC0OaJhEUQBEEQhDZPJCyCIAiCILR5ImERBEEQBKHNEwmLIAiCIAhtnkhYBEEQBEFo80TCIgiCIAhCmycSFkEQBEEQ2jyRsAiCIAiC0OadE6s1K4oCeJepFgRBEATh7HD0ffvo+/iJnBMJS3V1NQAxMTGtHIkgCIIgCM1VXV2Nv7//CctISlPSmjbO4/GQl5eHyWRCkiSqqqqIiYkhOzsbPz+/1g5PaIC4R22fuEdtn7hHbZu4P41TFIXq6mqioqKQ5RP3UjknalhkWSY6Orredj8/P/FH0saJe9T2iXvU9ol71LaJ+3NijdWsHCU63QqCIAiC0OaJhEUQBEEQhDbvnExYdDodzz33HDqdrrVDEY5D3KO2T9yjtk/co7ZN3J+WdU50uhUEQRAE4dx2TtawCIIgCIJwbhEJiyAIgiAIbZ5IWARBEARBaPNEwiIIgiAIQpt3ziUsBw4cYPz48YSEhODn58eQIUP466+/6pSRJKne1w8//NBKEZ9/mnKPjiotLSU6OhpJkqioqDizgZ7HGrtHpaWlXHzxxURFRaHT6YiJieH+++8X63mdIY3dn+3btzN58mRiYmIwGAwkJiby9ttvt2LE55+mvM49+OCD9OnTB51OR69evVon0LPIOZewXHbZZbhcLpYuXcrmzZvp2bMnl112GQUFBXXKffHFF+Tn59d+TZgwoXUCPg819R4B3HbbbSQnJ7dClOe3xu6RLMuMHz+e33//nQMHDvDll1+yePFi7r777laO/PzQ2P3ZvHkzYWFhfPvtt+zevZunnnqKadOm8d5777Vy5OePpr7O3XrrrVxzzTWtFOVZRjmHFBcXK4CyYsWK2m1VVVUKoCxatKh2G6DMnj27FSIUmnqPFEVRPvjgA2XYsGHKkiVLFEApLy8/w9Gen5pzj/7u7bffVqKjo89EiOe1k70/9957rzJ8+PAzEeJ5r7n36LnnnlN69ux5BiM8O51TNSzBwcF06dKFr7/+GrPZjMvl4qOPPiIsLIw+ffrUKXvfffcREhJC//79+fzzz5u0tLVw6pp6j/bs2cOLL77I119/3eiCWELLas7/0VF5eXn88ssvDBs27AxHe/45mfsDUFlZSVBQ0BmM9Px1svdIaERrZ0wtLTs7W+nTp48iSZKiUqmUyMhIZcuWLXXKvPjii8qqVauULVu2KK+99pqi0+mUt99+u5UiPv80do9sNpuSnJysfPPNN4qiKMpff/0laljOsKb8HymKolx77bWKwWBQAGXcuHGK1WpthWjPP029P0etXr1aUavVyoIFC85glOe35twjUcPSNGfFR9cnn3yywY6yf//at28fiqJw3333ERYWxsqVK9mwYQMTJkxg3Lhx5Ofn1x7vmWeeYfDgwfTu3ZsnnniCxx9/nNdff70Vr/Ds15L3aNq0aSQmJnLDDTe08lWdW1r6/wjgzTffZMuWLfz222+kpaUxderUVrq6s9/puD8Au3btYvz48Tz33HOMHj26Fa7s3HG67pHQNGfF1PzFxcWUlpaesEyHDh1YuXIlo0ePpry8vM5S3gkJCdx22208+eSTDe77559/ctlll2Gz2cSaDyepJe9Rr1692LlzJ5IkAaAoCh6PB5VKxVNPPcULL7xwWq/lXHW6/49WrVrF0KFDycvLIzIyskVjPx+cjvuzZ88ehg8fzu23387LL7982mI/X5yu/6Hnn3+eX3/9lW3btp2OsM8Z6tYOoClCQ0MJDQ1ttJzFYgGo1+dBlmU8Hs9x99u2bRuBgYEiWTkFLXmPfv75Z6xWa+1jGzdu5NZbb2XlypV07NixBaM+v5zu/6Ojj9nt9lOI8vzV0vdn9+7djBgxgilTpohkpYWc7v8h4cTOioSlqVJTUwkMDGTKlCk8++yzGAwGPvnkE9LT07n00ksB+OOPPygsLGTgwIHo9XoWLVrEK6+8wmOPPdbK0Z8fmnKP/jcpKSkpASAxMZGAgIAzHfJ5pyn3aO7cuRQWFtKvXz98fX3ZvXs3//jHPxg8eDDx8fGtewHnuKbcn127djFixAjGjBnD1KlTa4fSqlSqJr3hCqemKfcI4NChQ9TU1FBQUIDVaq2tYUlKSkKr1bZS9G1Y63WfOT02btyojB49WgkKClJMJpMycOBAZe7cubWPz5s3T+nVq5fi6+urGI1GpWfPnsqHH36ouN3uVoz6/NLYPfpfotPtmdfYPVq6dKmSmpqq+Pv7K3q9XklISFCeeOIJcY/OkMbuz3PPPacA9b7i4uJaL+jzTFNe54YNG9bgfUpPT2+doNu4s6IPiyAIgiAI57ezYpSQIAiCIAjnN5GwCIIgCILQ5omERRAEQRCENk8kLIIgCIIgtHkiYREEQRAEoc0TCYsgCIIgCG2eSFgEQRAEQWjzRMIiCIIgCEKbJxIWQRAEQRDaPJGwCIIgCILQ5omERRAEQRCENk8kLIIgCIIgtHn/D4taiorlZkT/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.02))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5269a224-4a97-417c-99af-6a98df0087ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "working on avg unit-to-HDcp dist for HD 1602\n",
      "working on avg unit-to-HDcp dist for HD 2402\n",
      "working on avg unit-to-HDcp dist for HD 3203\n",
      "working on avg unit-to-HDcp dist for HD 4004\n",
      "working on avg unit-to-HDcp dist for HD 4806\n",
      "working on avg unit-to-HDcp dist for HD 5606\n",
      "working on avg unit-to-HDcp dist for HD 6406\n",
      "working on avg unit-to-HDcp dist for HD 7206\n",
      "working on avg unit-to-HDcp dist for HD 8006\n",
      "working on avg unit-to-HDcp dist for HD 8807\n",
      "all unit-toHDcp distances computed\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([]) #set([9776,9777,9778] )  #lock in the clusters  #UPDATE THIS FOR EACH STATE - see CCB report 3 blocks up\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "6e2e44bd-a084-42f6-b533-5dc6d21912a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 187 HDs\n",
      "working on HD 297 cantFill 0 of 187 .Time is now 0\n",
      "working on HD 6875 cantFill 20 of 187 .Time is now 0\n",
      "working on HD 319 cantFill 40 of 187 .Time is now 9\n",
      "working on HD 707 cantFill 60 of 187 .Time is now 10\n",
      "working on HD 3044 cantFill 80 of 187 .Time is now 10\n",
      "working on HD 8238 cantFill 100 of 187 .Time is now 10\n",
      "working on HD 510 cantFill 120 of 187 .Time is now 15\n",
      "working on HD 2504 cantFill 140 of 187 .Time is now 15\n",
      "working on HD 3715 cantFill 160 of 187 .Time is now 15\n",
      "working on HD 8257 cantFill 180 of 187 .Time is now 16\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 1.00687 0.10809\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        if len(remainingHDborderSet) > 0: #for narrow peninsulas, separated border units may connect\n",
    "                            #this if statement was added for legisl OH\n",
    "                            HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                            HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e26c12d-181a-4e7b-8b37-10b371e8c22e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a61ea86-0982-4098-8217-68cebc9d77c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "ca9ce3cb-c150-4f39-ae55-f781d39d7f57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "c32dd108-073b-48aa-99ce-fea50c8d023c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f58fe2d0-3c62-412f-8984-ff5d67ea9b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 0\n",
      "working on HD 500\n",
      "working on HD 1000\n",
      "working on HD 1500\n",
      "working on HD 2000\n",
      "working on HD 2500\n",
      "working on HD 3000\n",
      "working on HD 3500\n",
      "working on HD 4000\n",
      "working on HD 4500\n",
      "working on HD 5000\n",
      "working on HD 5500\n",
      "working on HD 6000\n",
      "working on HD 6500\n",
      "working on HD 7000\n",
      "working on HD 7500\n",
      "working on HD 8000\n",
      "working on HD 8500\n",
      "out of 8934 total HDs, there were 8347 180 0 407 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "1ef683a2-4429-4a91-b1ea-a9b0950dea22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on current HD diam for HD number 0\n",
      "working on current HD diam for HD number 800\n",
      "working on current HD diam for HD number 1602\n",
      "working on current HD diam for HD number 2402\n",
      "working on current HD diam for HD number 3203\n",
      "working on current HD diam for HD number 4004\n",
      "working on current HD diam for HD number 4806\n",
      "working on current HD diam for HD number 5606\n",
      "working on current HD diam for HD number 6406\n",
      "working on current HD diam for HD number 7206\n",
      "working on current HD diam for HD number 8006\n",
      "working on current HD diam for HD number 8807\n",
      "here is the histogram of HD diameter = sqrt(area)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDdiam = [0. for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on current HD diam for HD number\",t)\n",
    "    HDdiam[t] = (HDpoly[t].intersection(MAP) ).area ** 0.5\n",
    "plt.hist([HDdiam[t] for t in popHDlist])\n",
    "print(\"here is the histogram of HD diameter = sqrt(area)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "45e3c17c-8c16-4bc5-b7fc-25b9250ed11a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we will regrow all disco'd HDs off by more than 5959 but less than 29796\n",
      "... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\n",
      "This is a total of 587 HDs to triage in this block\n",
      "working on swell-filling discontig HD 55 our 1 th HD swellable(?) of 587 0.0 sec elapsed\n",
      "working on swell-filling discontig HD 1407 our 41 th HD swellable(?) of 587 2.597 sec elapsed\n",
      "working on swell-filling discontig HD 2436 our 81 th HD swellable(?) of 587 6.81 sec elapsed\n",
      "working on swell-filling discontig HD 4071 our 121 th HD swellable(?) of 587 10.95 sec elapsed\n",
      "working on swell-filling discontig HD 8230 our 161 th HD swellable(?) of 587 13.526 sec elapsed\n",
      "working on swell-filling discontig HD 449 our 201 th HD swellable(?) of 587 18.621 sec elapsed\n",
      "working on swell-filling discontig HD 1046 our 241 th HD swellable(?) of 587 34.32 sec elapsed\n",
      "working on swell-filling discontig HD 1609 our 281 th HD swellable(?) of 587 40.769 sec elapsed\n",
      "working on swell-filling discontig HD 2415 our 321 th HD swellable(?) of 587 42.677 sec elapsed\n",
      "working on swell-filling discontig HD 3102 our 361 th HD swellable(?) of 587 49.665 sec elapsed\n",
      "working on swell-filling discontig HD 3746 our 401 th HD swellable(?) of 587 63.695 sec elapsed\n",
      "working on swell-filling discontig HD 3941 our 441 th HD swellable(?) of 587 67.446 sec elapsed\n",
      "working on swell-filling discontig HD 4559 our 481 th HD swellable(?) of 587 72.55 sec elapsed\n",
      "working on swell-filling discontig HD 5296 our 521 th HD swellable(?) of 587 79.928 sec elapsed\n",
      "working on swell-filling discontig HD 7707 our 561 th HD swellable(?) of 587 95.403 sec elapsed\n",
      "we partially regrew 467 HDs, leaving 120 to regrow from scratch\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE CANSWELL CODE\n",
    "print(\"Here, we will regrow all disco'd HDs off by more than\",int(aDP-minPostFixPop),\"but less than\",int(0.25*aDP) )\n",
    "print(\"... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\")\n",
    "HDnAddedUnits = [0 for t in range(nHDs)]\n",
    "maxGap = 0.9 * np.median(unitPop)  #set a reasonable gap prior to picking the last block\n",
    "HDdiam = [HDpoly[t].area ** 0.5 for t in range(nHDs) ] #in case not defined before\n",
    "badDiscoList = list()\n",
    "nCanSwell = 0\n",
    "triageList = discontigOnly + bothProblems\n",
    "print(\"This is a total of\",len(triageList),\"HDs to triage in this block\")\n",
    "startTime = time.time()\n",
    " \n",
    "for i,t in enumerate(triageList): #canSwell\n",
    "    if i%40 == 0:\n",
    "        SEC = r3(time.time()-startTime)\n",
    "        print(\"working on swell-filling discontig HD\",t,\"our\",i+1,\"th HD swellable(?) of\",len(triageList),SEC,\"sec elapsed\" )\n",
    "    distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    if contigPop < 0.50 * aDP or contigPop > 2.*aDP - minPostFixPop:  #formerly < 0.75 aDP\n",
    "        badDiscoList.append(t)\n",
    "    else: #rebuild from this big piece\n",
    "        nCanSwell +=1\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "        stillGoing = True\n",
    "\n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        HDnAddedUnits[t] = len(addedList)\n",
    "    \n",
    "badDiscoList += enclaveOnly\n",
    "print(\"we partially regrew\",nCanSwell,\"HDs, leaving\",len(badDiscoList),\"to regrow from scratch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f57325dd-523c-466a-80cb-8e10b739b4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous avg use and its sd are 1.00687 0.10809\n",
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00518 0.10124\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"previous avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "0ce918e1-0d30-4930-9fb8-0769d793683c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 1001 time is now 2\n",
      "working on HD 2002 time is now 3\n",
      "working on HD 3003 time is now 5\n",
      "working on HD 4004 time is now 7\n",
      "working on HD 5006 time is now 9\n",
      "working on HD 6006 time is now 11\n",
      "working on HD 7006 time is now 13\n",
      "working on HD 8006 time is now 15\n",
      "out of 8934 total HDs, there were 8814.0 contiguous and 8760.0 complement-contiguous HDs\n",
      "64 HDs had both discontiguity problems, while enclave-only = 110 and discontig only= 56\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 120 110\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "463db75a-694e-4c7d-9693-e10f3b4d65db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 113227 district pop vs 119186 target\n",
      "we'll also trim any HD with total islands' pop <= 2383\n",
      "working on HD 171\n",
      "working on HD 8196\n",
      "Out of 120 discontig HDs 120 had discontig HDs.\n",
      "Of these, 0 won't be under 113227 after all minor discontigys were shed, while 120 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "There are 120 HDs still with both discontinuity problems\n",
      "Additionally, 0 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%100 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for t in sPgenerator:    \n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "39d37c15-759f-462c-b503-047b6b5e1554",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180 407 120\n"
     ]
    }
   ],
   "source": [
    "print(len(discontigOnly),len(bothProblems), len(badDiscoList))\n",
    "#print((set(discontigOnly).union(set(bothProblems)) ) .difference(set(badDiscoList)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "2e54bac4-c327-473d-bbb4-257486bddd20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is 120\n",
      "swell-generating HD 171 our 1 th HD we must regrow out of 120 0 sec elapsed\n",
      "swell-generating HD 928 our 11 th HD we must regrow out of 120 2 sec elapsed\n",
      "swell-generating HD 1658 our 21 th HD we must regrow out of 120 4 sec elapsed\n",
      "swell-generating HD 2354 our 31 th HD we must regrow out of 120 6 sec elapsed\n",
      "swell-generating HD 2402 our 41 th HD we must regrow out of 120 7 sec elapsed\n",
      "swell-generating HD 2430 our 51 th HD we must regrow out of 120 7 sec elapsed\n",
      "swell-generating HD 2747 our 61 th HD we must regrow out of 120 10 sec elapsed\n",
      "swell-generating HD 3108 our 71 th HD we must regrow out of 120 11 sec elapsed\n",
      "swell-generating HD 3849 our 81 th HD we must regrow out of 120 12 sec elapsed\n",
      "swell-generating HD 5881 our 91 th HD we must regrow out of 120 14 sec elapsed\n",
      "swell-generating HD 8196 our 101 th HD we must regrow out of 120 16 sec elapsed\n",
      "swell-generating HD 8396 our 111 th HD we must regrow out of 120 18 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%10 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "b78199da-ac7c-4cfb-ac0b-26363a8a8c56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prev avg use and its sd are 1.00518 0.10124\n",
      "defining and displaying current unit use after most recent manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.0053 0.09959\n"
     ]
    },
    {
     "data": {
      "image/png": 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LAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMN6YSC8AwPBrbO0Oy7wp4+PkTHaEZW4AoxvBAowiKePj5Bhr05LqhrDM7xhr066lhUQLgJAjWIBRxJns0K6lhWr39YV87sbWbi2pblC7r49gARByBAswyjiTHQQFgBGHm24BAIDxCBYAAGC8IQXL5s2blZ2drfj4eOXn52vPnj0nHfvee+/p5ptvVnZ2tmJiYlRVVXXWcwIAgNEl6GCprq6W2+1WRUWF9u7dqxkzZqioqEitra2Djj969KgmTZqkNWvWKCMjIyRzAgCA0SXoYNm4caMWLVqksrIyTZ06VVu3btW4ceO0bdu2QcdfccUVeuSRR7RgwQLZ7faQzAkAAEaXoIKlr69P9fX1crlcX04QGyuXy6W6urohLWAoc/b29qqrqytgAwAA0SuoYGlra1N/f7/S09MD9qenp8vr9Q5pAUOZs7KyUklJSf4tKytrSL83AAAYGUbku4TKy8vV2dnp35qamiK9JAAAEEZBPTguNTVVNptNLS0tAftbWlpOekNtOOa02+0nvR8GAABEn6CusMTFxWnmzJnyeDz+fQMDA/J4PCooKBjSAsIxJwAAiC5BP5rf7XartLRUs2bNUl5enqqqquTz+VRWViZJKikpkdPpVGVlpaQvbqp9//33/b9ubm5WQ0ODEhISlJOTc0ZzAgCA0S3oYCkuLtahQ4e0cuVKeb1e5ebmqqamxn/T7IEDBxQb++WFm4MHD+ryyy/3f7x+/XqtX79ehYWFqq2tPaM5AQDA6DakH364ePFiLV68eNDPHY+Q47Kzs2VZ1lnNCQAARrcR+S4hAAAwuhAsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOONifQCgOOaO3rU7usL+byNrd0hnxMAMLwIFhihuaNHrg1vqedYf1jmd4y1KWV8XFjmBgCEH8ECI7T7+tRzrF9VxbnKSUsI+fwp4+PkTHaEfF4AwPAgWGCUnLQETXMmRXoZAADDcNMtAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIw3JtILABBdGlu7Qz5nyvg4OZMdIZ8XwMhBsAAIiZTxcXKMtWlJdUPI53aMtWnX0kKiBRjFCBYAIeFMdmjX0kK1+/pCOm9ja7eWVDeo3ddHsACjGMECIGScyQ6iAkBYcNMtAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4QwqWzZs3Kzs7W/Hx8crPz9eePXtOOf6ll17SJZdcovj4eF122WV67bXXAj7/gx/8QDExMQHb3Llzh7I0AAAQhYIOlurqarndblVUVGjv3r2aMWOGioqK1NraOuj4v/zlL1q4cKF++MMfat++fZo/f77mz5+vv//97wHj5s6dq3/961/+7YUXXhjaKwIAAFEn6GDZuHGjFi1apLKyMk2dOlVbt27VuHHjtG3btkHHP/roo5o7d66WLVumSy+9VKtXr9bXvvY1bdq0KWCc3W5XRkaGf0tJSTnpGnp7e9XV1RWwAQCA6BVUsPT19am+vl4ul+vLCWJj5XK5VFdXN+gxdXV1AeMlqaio6ITxtbW1SktL05QpU3TXXXfp8OHDJ11HZWWlkpKS/FtWVlYwLwMAAIwwQQVLW1ub+vv7lZ6eHrA/PT1dXq930GO8Xu9px8+dO1fPPfecPB6P1q5dq7feekvXX3+9+vv7B52zvLxcnZ2d/q2pqSmYlwEAAEaYMZFegCQtWLDA/+vLLrtM06dP10UXXaTa2lpde+21J4y32+2y2+3DuUQAABBBQV1hSU1Nlc1mU0tLS8D+lpYWZWRkDHpMRkZGUOMladKkSUpNTVVjY2MwywMAAFEqqGCJi4vTzJkz5fF4/PsGBgbk8XhUUFAw6DEFBQUB4yXpzTffPOl4Sfrss890+PBhTZw4MZjlAQCAKBX0u4Tcbreeeuopbd++Xfv379ddd90ln8+nsrIySVJJSYnKy8v943/605+qpqZGGzZs0AcffKBVq1bp3Xff1eLFiyVJ3d3dWrZsmXbv3q1PP/1UHo9HN910k3JyclRUVBSilwkAAEayoO9hKS4u1qFDh7Ry5Up5vV7l5uaqpqbGf2PtgQMHFBv7ZQddeeWV2rFjhx566CE98MADmjx5sl555RVNmzZNkmSz2fTXv/5V27dvV0dHhzIzMzVnzhytXr2a+1QAAICkId50u3jxYv8Vkv9WW1t7wr5bbrlFt9xyy6DjHQ6HXn/99aEsAwAAjBL8LCEAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYb0iP5sfo1tzRo3ZfX0jnbGztDul8iD7h+jOSMj5OzmRHWOYGEDoEC4LS3NEj14a31HOsP+RzO8balDI+LuTzYmRLGR8nx1ibllQ3hGV+x1ibdi0tJFoAwxEsCEq7r089x/pVVZyrnLSEkM7Nv3QxGGeyQ7uWFob8qp70xVWbJdUNavf18WcPMBzBgiHJSUvQNGdSpJeBUcKZ7CAogFGOm24BAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPHGRHoBABBpja3dYZk3ZXycnMmOsMwNjDYES5Rq7uhRu68v5POG6z/sQCSkjI+TY6xNS6obwjK/Y6xNu5YWEi1ACBAsUai5o0euDW+p51h/WOZ3jLUpZXxcWOYGhpMz2aFdSwvDFvdLqhvU7usjWIAQIFiiULuvTz3H+lVVnKuctISQz89lbkQTZ7KDP8/ACECwRLGctARNcyZFehkAAJw13iUEAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMx09rjrDmjh61+/pCOmdja3dI5wMwdOH4+5gyPk7OZEfI5wVMRrBEUHNHj1wb3lLPsf6Qz+0Ya1PK+LiQzwvgzKSMj5NjrE1LqhtCPrdjrE27lhYSLRhVCJYIavf1qedYv6qKc5WTlhDSufkXGBBZzmSHdi0tDMsV1CXVDWr39fF3HKMKwWKAnLQETXMmRXoZAELMmewgKoAQ4aZbAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA83iV0BsLxcDeJB7wBAHCmCJbTCOfD3SQe8AbALOH6BxrPhsLZIlhOI5wPd5P4SwxgaMJxhfawr093/t/6sD19m6fz4mwQLGeIh7sBMEE4H/kvfREW22/P01dCeOWXp/MiFIYULJs3b9Yjjzwir9erGTNm6PHHH1deXt5Jx7/00ktasWKFPv30U02ePFlr167VDTfc4P+8ZVmqqKjQU089pY6ODl111VXasmWLJk+ePJTlAUDUCtcj/4/jqi9MFXSwVFdXy+12a+vWrcrPz1dVVZWKior04YcfKi0t7YTxf/nLX7Rw4UJVVlbq29/+tnbs2KH58+dr7969mjZtmiRp3bp1euyxx7R9+3ZdeOGFWrFihYqKivT+++8rPj7+7F8lAESRkfrI/3C90SBckRWu+3kkwnAoYizLsoI5ID8/X1dccYU2bdokSRoYGFBWVpbuueceLV++/ITxxcXF8vl8+u1vf+vf9/Wvf125ubnaunWrLMtSZmamli5dqnvvvVeS1NnZqfT0dD377LNasGDBCXP29vaqt7fX/3FnZ6fOP/98NTU1KTExMZiXc1rvH+zUrf+zWy/+n69raibfEgKAYB3s6NF3Nr2tz48NhGX++LGxqlpwuSaMGxuyOf999JiW7Nw3otYcbucm2HVuYmgvInR1dSkrK0sdHR1KSjrN11grCL29vZbNZrNefvnlgP0lJSXWd77znUGPycrKsn75y18G7Fu5cqU1ffp0y7Is65///Kclydq3b1/AmG984xvWT37yk0HnrKiosCSxsbGxsbGxRcHW1NR02gYJ6ltCbW1t6u/vV3p6esD+9PR0ffDBB4Me4/V6Bx3v9Xr9nz++72Rj/lt5ebncbrf/44GBAf373//WV77yFcXExATzkka142UbjitTOHOch8jjHJiB8xB5w30OLMvSkSNHlJmZedqxI/JdQna7XXa7PWBfcnJyZBYTBRITE/mPgwE4D5HHOTAD5yHyhvMcnPZbQf8rqEfzp6amymazqaWlJWB/S0uLMjIyBj0mIyPjlOOP/28wcwIAgNElqGCJi4vTzJkz5fF4/PsGBgbk8XhUUFAw6DEFBQUB4yXpzTff9I+/8MILlZGRETCmq6tL77zzzknnBAAAo0vQ3xJyu90qLS3VrFmzlJeXp6qqKvl8PpWVlUmSSkpK5HQ6VVlZKUn66U9/qsLCQm3YsEE33nijdu7cqXfffVdPPvmkJCkmJkZLlizRz3/+c02ePNn/tubMzEzNnz8/dK8UJ7Db7aqoqDjh22sYXpyHyOMcmIHzEHkmn4Og39YsSZs2bfI/OC43N1ePPfaY8vPzJUnf/OY3lZ2drWeffdY//qWXXtJDDz3kf3DcunXrBn1w3JNPPqmOjg7Nnj1bTzzxhC6++OKzf4UAAGDEG1KwAAAADKeg7mEBAACIBIIFAAAYj2ABAADGI1gAAIDxCJYot3nzZmVnZys+Pl75+fnas2fPKcdXVVVpypQpcjgcysrK0s9+9jN9/vnnw7Ta6PPHP/5R8+bNU2ZmpmJiYvTKK6+c9pja2lp97Wtfk91uV05OTsA77jA0wZ6HX//617ruuut07rnnKjExUQUFBXr99deHZ7FRaih/F47785//rDFjxig3Nzds6xsthnIeent79eCDD+qCCy6Q3W5Xdna2tm3bFv7F/heCJYpVV1fL7XaroqJCe/fu1YwZM1RUVKTW1tZBx+/YsUPLly9XRUWF9u/fr6efflrV1dV64IEHhnnl0cPn82nGjBnavHnzGY3/5JNPdOONN+pb3/qWGhoatGTJEt1xxx18sTxLwZ6HP/7xj7ruuuv02muvqb6+Xt/61rc0b9487du3L8wrjV7BnoPjOjo6VFJSomuvvTZMKxtdhnIebr31Vnk8Hj399NP68MMP9cILL2jKlClhXOVJnPbHI2LEysvLs3784x/7P+7v77cyMzOtysrKQcf/+Mc/tq655pqAfW6327rqqqvCus7RQtIJP+n8v913333WV7/61YB9xcXFVlFRURhXNrqcyXkYzNSpU62HH3449AsahYI5B8XFxdZDDz1kVVRUWDNmzAjrukabMzkPv/vd76ykpCTr8OHDw7OoU+AKS5Tq6+tTfX29XC6Xf19sbKxcLpfq6uoGPebKK69UfX29/9tGH3/8sV577bWAh/whvOrq6gLOmSQVFRWd9JxheAwMDOjIkSOaMGFCpJcyqjzzzDP6+OOPVVFREemljFq/+c1vNGvWLK1bt05Op1MXX3yx7r33XvX09Az7WkbkT2vG6bW1tam/v1/p6ekB+9PT0/XBBx8Mesxtt92mtrY2zZ49W5Zl6T//+Y/uvPNOviU0jLxe76DnrKurSz09PXI4HBFa2ei2fv16dXd369Zbb430UkaNjz76SMuXL9ef/vQnjRnDl6pI+fjjj/X2228rPj5eL7/8stra2nT33Xfr8OHDeuaZZ4Z1LVxhgV9tba1+8Ytf6IknntDevXv161//Wq+++qpWr14d6aUBEbNjxw49/PDDevHFF5WWlhbp5YwK/f39uu222/Twww/zI1oibGBgQDExMXr++eeVl5enG264QRs3btT27duH/SoL2RqlUlNTZbPZ1NLSErC/paVFGRkZgx6zYsUKff/739cdd9whSbrsssvk8/n0ox/9SA8++KBiY+nbcMvIyBj0nCUmJnJ1JQJ27typO+64Qy+99NIJ36pD+Bw5ckTvvvuu9u3bp8WLF0v64gunZVkaM2aM3njjDV1zzTURXuXoMHHiRDmdTiUlJfn3XXrppbIsS5999pkmT548bGvhK1CUiouL08yZM+XxePz7BgYG5PF4VFBQMOgxR48ePSFKbDabpC9+QCXCr6CgIOCcSdKbb7550nOG8HnhhRdUVlamF154QTfeeGOklzOqJCYm6m9/+5saGhr825133qkpU6aooaHB/8N2EX5XXXWVDh48qO7ubv++f/zjH4qNjdV55503rGvhCksUc7vdKi0t1axZs5SXl6eqqir5fD6VlZVJkkpKSuR0OlVZWSlJmjdvnjZu3KjLL79c+fn5amxs1IoVKzRv3jx/uCA43d3damxs9H/8ySefqKGhQRMmTND555+v8vJyNTc367nnnpMk3Xnnndq0aZPuu+8+3X777fr973+vF198Ua+++mqkXkJUCPY87NixQ6WlpXr00UeVn58vr9crSXI4HAH/0sSZC+YcxMbGatq0aQHHp6WlKT4+/oT9CE6wfxduu+02rV69WmVlZXr44YfV1tamZcuW6fbbbx/+q76RfZMSwu3xxx+3zj//fCsuLs7Ky8uzdu/e7f9cYWGhVVpa6v/42LFj1qpVq6yLLrrIio+Pt7Kysqy7777bam9vH/6FR4k//OEPlqQTtuP/v5eWllqFhYUnHJObm2vFxcVZkyZNsp555plhX3e0CfY8FBYWnnI8gjeUvwv/P97WHBpDOQ/79++3XC6X5XA4rPPOO89yu93W0aNHh33tMZbFtX4AAGA27mEBAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgvP8HIfGrsQFtL+4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"prev avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after most recent manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "a2242181-3a07-40b7-ae56-bbe47bd2dc79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(not-so-)final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 500 time is now 72 sec\n",
      "working on HD 1001 time is now 143 sec\n",
      "working on HD 1502 time is now 171 sec\n",
      "working on HD 2002 time is now 188 sec\n",
      "working on HD 2502 time is now 212 sec\n",
      "working on HD 3003 time is now 263 sec\n",
      "working on HD 3503 time is now 312 sec\n",
      "working on HD 4004 time is now 417 sec\n",
      "working on HD 4505 time is now 434 sec\n",
      "working on HD 5006 time is now 456 sec\n",
      "working on HD 5506 time is now 499 sec\n",
      "working on HD 6006 time is now 536 sec\n",
      "working on HD 6506 time is now 537 sec\n",
      "working on HD 7006 time is now 543 sec\n",
      "working on HD 7506 time is now 544 sec\n",
      "working on HD 8006 time is now 582 sec\n",
      "working on HD 8507 time is now 598 sec\n",
      "out of 8934 total HDs, there were 8824 0 110 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 614 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "623fb818-67d9-458d-b854-4c70dfa449ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 125145 district pop vs 119186 target\n",
      "working on enclave-y HD 33 . We have evaluated 0 of 8042 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 7633 . We have evaluated 100 of 8042 enclavy HDs.Time is now 1\n",
      "Out of 8042 HDs with enclaves 110 had enclaves.\n",
      "Of these, 110 wouldn't be over 125145 if all enclaves filled, while 0 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#OPTIONAL if still enclaves -- rerun the CANFILL CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(enclaveOnly):  #internals + edgers):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1692c13f-175a-4e08-bd41-a6eaeb432894",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note: see original vanillaHD-OH (Congressional) if we have stubborn ones to fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "18f46c40-7f28-4714-962f-0b462ff97b8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final ??? check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 500 time is now 1 sec\n",
      "working on HD 1001 time is now 1 sec\n",
      "working on HD 1502 time is now 2 sec\n",
      "working on HD 2002 time is now 3 sec\n",
      "working on HD 2502 time is now 4 sec\n",
      "working on HD 3003 time is now 5 sec\n",
      "working on HD 3503 time is now 6 sec\n",
      "working on HD 4004 time is now 7 sec\n",
      "working on HD 4505 time is now 8 sec\n",
      "working on HD 5006 time is now 9 sec\n",
      "working on HD 5506 time is now 10 sec\n",
      "working on HD 6006 time is now 10 sec\n",
      "working on HD 6506 time is now 11 sec\n",
      "working on HD 7006 time is now 12 sec\n",
      "working on HD 7506 time is now 14 sec\n",
      "working on HD 8006 time is now 15 sec\n",
      "working on HD 8507 time is now 16 sec\n",
      "out of 8934 total HDs, there were 8934 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 17 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"final ??? check -- any more disco's ?\")  #run again if we ran above block to fill minor enclaves\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "de4d8af8-cc64-4215-9b95-f4c6a2bd03af",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedAgainUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "f4bb76c7-0ae0-40cb-9c6a-ee436dfc30c4",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "HDunitList = [savedAgainUnitList[t].copy() for t in range(nHDs)]  #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "1e983c03-777a-4e81-bed0-34a620175f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7e127ba-80ba-4618-b7a2-91a66c2abd7e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "9bc31b5a-992d-4a38-b9fe-3470b55e1e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"legisl60_1p5ContigButOffPopUnit.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "7b9b68ef-6ae9-4d80-895d-41a86f48a55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n"
     ]
    }
   ],
   "source": [
    "print(\"hi\")  #see MN for code to boost any under-used CCBs via border linking strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6150cac-2249-415b-a364-edf55dc441ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "114705bb-9640-45f1-9f25-030030efaa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below here -- working on tightening use distro while squaring up pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "5d7d8519-eb3d-4345-9ac6-ab888e61c348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.00552 0.0995\n"
     ]
    }
   ],
   "source": [
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "b1971853-bda7-49a4-9f25-1bdfd5e75aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "working on avg unit-to-HDcp dist for HD 1602\n",
      "working on avg unit-to-HDcp dist for HD 2402\n",
      "working on avg unit-to-HDcp dist for HD 3203\n",
      "working on avg unit-to-HDcp dist for HD 4004\n",
      "working on avg unit-to-HDcp dist for HD 4806\n",
      "working on avg unit-to-HDcp dist for HD 5606\n",
      "working on avg unit-to-HDcp dist for HD 6406\n",
      "working on avg unit-to-HDcp dist for HD 7206\n",
      "working on avg unit-to-HDcp dist for HD 8006\n",
      "working on avg unit-to-HDcp dist for HD 8807\n"
     ]
    }
   ],
   "source": [
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "f5b99900-614a-47df-b7de-5d59fffd6d3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAwYlk7TtLW1qaamRk6n84dfEBoqp9Op6urqTrd59913lZ6errlz5yomJkbjxo3T0qVL1d7e3uXzeDweud3uDg8AADAwWYqRxsZGtbe3KyYmpsPymJgY1dfXd7rNsWPH9M4776i9vV07duzQokWLtGLFCr388stdPk9xcbEcDof/kZCQYGVMAAAQRPr8bhqv16vo6Gi9+eabSklJUXZ2thYsWKCysrIutykoKFBTU5P/cfLkyb4eEwAAGGLpmpGoqCiFhYWpoaGhw/KGhgbFxsZ2us3w4cN1zTXXKCwszL9s7Nixqq+vV1tbmyIiIi7ZxmazyWazWRkNAAAEKUtHRiIiIpSSkqKqqir/Mq/Xq6qqKqWnp3e6zeTJk3X06FF5vV7/siNHjmj48OGdhggAABhcLJ+mcblceuutt/T222/r4MGDevLJJ9Xa2uq/uyYnJ0cFBQX+9Z988kmdO3dO8+bN05EjR1RZWamlS5dq7ty5vfcqAABA0LJ8a292drbOnj2rxYsXq76+XsnJydq5c6f/ota6ujqFhv7QOAkJCdq1a5fmz5+vCRMmKD4+XvPmzdMLL7zQe68CgF9ifuUV1zmxLDMAkwBA94T4fD6f6SGuxO12y+FwqKmpSXa73fQ4CALdeUMezIgRAIHQ3fdvvpsGAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwKNz0AYEVifqXpEQAAvYwjIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGMXHwQODUHc+Vv/EsswATAIAHBkBAACGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwqkcxUlpaqsTEREVGRiotLU379u3r1nabNm1SSEiIZs6c2ZOnBQAAA5DlGKmoqJDL5VJhYaEOHDigpKQkZWRk6MyZM5fd7sSJE3ruuec0ZcqUHg8LAAAGHssxsnLlSj3++OPKy8vTHXfcobKyMl177bVau3Ztl9u0t7frV7/6lYqKinTrrbde1cAAAGBgsRQjbW1tqqmpkdPp/OEXhIbK6XSqurq6y+1+97vfKTo6Wr/5zW96PikAABiQwq2s3NjYqPb2dsXExHRYHhMTo0OHDnW6zYcffqg1a9aotra228/j8Xjk8Xj8P7vdbitjAgCAIGIpRqxqbm7Wo48+qrfeektRUVHd3q64uFhFRUV9OBmAK0nMr7ziOieWZQZgEgADnaUYiYqKUlhYmBoaGjosb2hoUGxs7CXrf/755zpx4oSysrL8y7xe78UnDg/X4cOHNWrUqEu2KygokMvl8v/sdruVkJBgZVQAABAkLMVIRESEUlJSVFVV5b891+v1qqqqSk8//fQl6//4xz/Wp59+2mHZwoUL1dzcrFWrVnUZGDabTTabzcpoAAAgSFk+TeNyuZSbm6tJkyYpNTVVJSUlam1tVV5eniQpJydH8fHxKi4uVmRkpMaNG9dh++uvv16SLlkOAAAGJ8sxkp2drbNnz2rx4sWqr69XcnKydu7c6b+ota6uTqGhfLArrOvONQoAgIEnxOfz+UwPcSVut1sOh0NNTU2y2+2mx0EfIUaCDxewAric7r5/cwgDAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAqHDTA2BwSMyvND0CAKCf4sgIAAAwiiMjAHqsO0e8TizLDMAkAIIZR0YAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBR4aYHADCwJeZXXnGdE8syAzAJgP6KIyMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABG8aFnAIzjg9GAwY0jIwAAwChiBAAAGEWMAAAAo3oUI6WlpUpMTFRkZKTS0tK0b9++Ltd96623NGXKFN1www264YYb5HQ6L7s+AAAYXCzHSEVFhVwulwoLC3XgwAElJSUpIyNDZ86c6XT9PXv26JFHHtEHH3yg6upqJSQk6IEHHtCpU6euengAABD8Qnw+n8/KBmlpabr77ru1evVqSZLX61VCQoKeeeYZ5efnX3H79vZ23XDDDVq9erVycnK69Zxut1sOh0NNTU2y2+1WxkU/0Z27JYDL4W4aIPh09/3b0q29bW1tqqmpUUFBgX9ZaGionE6nqquru/U7zp8/r++++07Dhg3rch2PxyOPx+P/2e12WxkTAUZoAACuhqXTNI2NjWpvb1dMTEyH5TExMaqvr+/W73jhhRcUFxcnp9PZ5TrFxcVyOBz+R0JCgpUxAQBAEAno3TTLli3Tpk2btG3bNkVGRna5XkFBgZqamvyPkydPBnBKAAAQSJZO00RFRSksLEwNDQ0dljc0NCg2Nvay2y5fvlzLli3T3/72N02YMOGy69psNtlsNiujAQCAIGXpyEhERIRSUlJUVVXlX+b1elVVVaX09PQut3vttde0ZMkS7dy5U5MmTer5tAAAYMCx/N00LpdLubm5mjRpklJTU1VSUqLW1lbl5eVJknJychQfH6/i4mJJ0quvvqrFixervLxciYmJ/mtLhg4dqqFDh/biSwEAAMHIcoxkZ2fr7NmzWrx4serr65WcnKydO3f6L2qtq6tTaOgPB1z++Mc/qq2tTQ899FCH31NYWKiXXnrp6qYHAABBz/LnjJjA54z0b9zai0Dgc0aA4NPd92++mwYAABhFjAAAAKOIEQAAYJTlC1gxuHA9CACgr3FkBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKW3sBBIXu3GbOR8YDwYkYATBgECxAcCJGBjE+0AwA0B9wzQgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRfOgZgEGFT2kF+h+OjAAAAKOIEQAAYBSnaQYovncGABAsODICAACMIkYAAIBRnKYBgP/BHTdAYHFkBAAAGMWRkSDExakAgIGEIyMAAMAoYgQAABhFjAAAAKO4ZqSf4XoQAMBgw5ERAABgFDECAACM4jQNAPQAH4wG9B6OjAAAAKOIEQAAYBQxAgAAjOKaEQDoI1xXAnQPR0YAAIBRxAgAADCK0zQBxKerAgBwKWKklxAaAHqC60oAYqRbCA0AAPoO14wAAACjODICAIMEp4TQXxEjAAA/ggUm9ChGSktL9fvf/1719fVKSkrS66+/rtTU1C7X37JlixYtWqQTJ07o9ttv16uvvqoZM2b0eGgAGEwIBAx0lmOkoqJCLpdLZWVlSktLU0lJiTIyMnT48GFFR0dfsv7HH3+sRx55RMXFxfr5z3+u8vJyzZw5UwcOHNC4ceN65UVcDS5OBTAQ8N8yBLMQn8/ns7JBWlqa7r77bq1evVqS5PV6lZCQoGeeeUb5+fmXrJ+dna3W1la99957/mX33HOPkpOTVVZW1q3ndLvdcjgcampqkt1utzLuFfEXGACs4SgMuqu779+Wjoy0tbWppqZGBQUF/mWhoaFyOp2qrq7udJvq6mq5XK4OyzIyMrR9+/Yun8fj8cjj8fh/bmpqknTxRfU2r+d8r/9OABjIuvPf4nGFu3rluf5dlNErv6c78/TWc3VHf5unr3z/78qVjntYipHGxka1t7crJiamw/KYmBgdOnSo023q6+s7Xb++vr7L5ykuLlZRUdElyxMSEqyMCwDoA44SnisQ+ts8V6O5uVkOh6PLP++Xd9MUFBR0OJri9Xp17tw53XjjjQoJCTE4WfBwu91KSEjQyZMne/3UFrqP/dA/sB/MYx/0D4HeDz6fT83NzYqLi7vsepZiJCoqSmFhYWpoaOiwvKGhQbGxsZ1uExsba2l9SbLZbLLZbB2WXX/99VZGxf9jt9v5i98PsB/6B/aDeeyD/iGQ++FyR0S+Z+kTWCMiIpSSkqKqqir/Mq/Xq6qqKqWnp3e6TXp6eof1Jen999/vcn0AADC4WD5N43K5lJubq0mTJik1NVUlJSVqbW1VXl6eJCknJ0fx8fEqLi6WJM2bN09Tp07VihUrlJmZqU2bNmn//v168803e/eVAACAoGQ5RrKzs3X27FktXrxY9fX1Sk5O1s6dO/0XqdbV1Sk09IcDLvfee6/Ky8u1cOFCvfjii7r99tu1ffv2fvEZIwOZzWZTYWHhJae7EFjsh/6B/WAe+6B/6K/7wfLnjAAAAPQmvrUXAAAYRYwAAACjiBEAAGAUMQIAAIwiRoJYaWmpEhMTFRkZqbS0NO3bt++y65eUlGjMmDEaMmSIEhISNH/+fH377bcBmnZg2rt3r7KyshQXF6eQkJDLfufS9/bs2aO77rpLNptNt912m9avX9/ncw5kVvfB1q1bNX36dN10002y2+1KT0/Xrl298z0qg1lP/i5876OPPlJ4eLiSk5P7bL7BoCf7wOPxaMGCBbr55ptls9mUmJiotWvX9v2w/4MYCVIVFRVyuVwqLCzUgQMHlJSUpIyMDJ05c6bT9cvLy5Wfn6/CwkIdPHhQa9asUUVFhV588cUATz6wtLa2KikpSaWlpd1a//jx48rMzNS0adNUW1urZ599VrNnz+bN8CpY3Qd79+7V9OnTtWPHDtXU1GjatGnKysrSJ5980seTDmxW98P3vv76a+Xk5OinP/1pH002ePRkHzz88MOqqqrSmjVrdPjwYW3cuFFjxozpwym74ENQSk1N9c2dO9f/c3t7uy8uLs5XXFzc6fpz58713X///R2WuVwu3+TJk/t0zsFEkm/btm2XXef555/33XnnnR2WZWdn+zIyMvpwssGjO/ugM3fccYevqKio9wcapKzsh+zsbN/ChQt9hYWFvqSkpD6dazDpzj7461//6nM4HL6vvvoqMENdBkdGglBbW5tqamrkdDr9y0JDQ+V0OlVdXd3pNvfee69qamr8p3KOHTumHTt2aMaMGQGZGRdVV1d32G+SlJGR0eV+Q9/zer1qbm7WsGHDTI8y6Kxbt07Hjh1TYWGh6VEGpXfffVeTJk3Sa6+9pvj4eI0ePVrPPfecvvnmm4DP0i+/tReX19jYqPb2dv+n3n4vJiZGhw4d6nSbWbNmqbGxUffdd598Pp8uXLigOXPmcJomwOrr6zvdb263W998842GDBliaLLBa/ny5WppadHDDz9sepRB5bPPPlN+fr7+8Y9/KDyctyITjh07pg8//FCRkZHatm2bGhsb9dRTT+mrr77SunXrAjoLR0YGiT179mjp0qV64403dODAAW3dulWVlZVasmSJ6dEAY8rLy1VUVKTNmzcrOjra9DiDRnt7u2bNmqWioiKNHj3a9DiDltfrVUhIiP785z8rNTVVM2bM0MqVK/X2228H/OgIORqEoqKiFBYWpoaGhg7LGxoaFBsb2+k2ixYt0qOPPqrZs2dLksaPH6/W1lY98cQTWrBgQYfvE0LfiY2N7XS/2e12jooE2KZNmzR79mxt2bLlklNn6FvNzc3av3+/PvnkEz399NOSLr4x+nw+hYeHa/fu3br//vsNTznwDR8+XPHx8XI4HP5lY8eOlc/n03/+8x/dfvvtAZuFd6AgFBERoZSUFFVVVfmXeb1eVVVVKT09vdNtzp8/f0lwhIWFSZJ8fD1RwKSnp3fYb5L0/vvvd7nf0Dc2btyovLw8bdy4UZmZmabHGXTsdrs+/fRT1dbW+h9z5szRmDFjVFtbq7S0NNMjDgqTJ0/W6dOn1dLS4l925MgRhYaGasSIEQGdhSMjQcrlcik3N1eTJk1SamqqSkpK1Nraqry8PElSTk6O4uPjVVxcLEnKysrSypUrNXHiRKWlpeno0aNatGiRsrKy/FEC61paWnT06FH/z8ePH1dtba2GDRumkSNHqqCgQKdOndKGDRskSXPmzNHq1av1/PPP67HHHtPf//53bd68WZWVlaZeQtCzug/Ky8uVm5urVatWKS0tTfX19ZKkIUOGdPg/RFhjZT+EhoZe8s3t0dHRioyM5Bvdr4LVvwuzZs3SkiVLlJeXp6KiIjU2Nuq3v/2tHnvsscAfqTV7Mw+uxuuvv+4bOXKkLyIiwpeamur75z//6f+zqVOn+nJzc/0/f/fdd76XXnrJN2rUKF9kZKQvISHB99RTT/n++9//Bn7wAeSDDz7wSbrk8f0/+9zcXN/UqVMv2SY5OdkXERHhu/XWW33r1q0L+NwDidV9MHXq1Muuj57pyd+F/x+39l69nuyDgwcP+pxOp2/IkCG+ESNG+Fwul+/8+fMBnz3E5+MYPQAAMIdrRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAqP8DIcw9Z/a/rBkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1leLi4pxl0aJFkiSXy6WVK1dq6NCh6t27tx544AGNGDFC77zzjrOPyMhILV26VJGRkfL7/brjjjs0ZswYPfroo05N9+7dlZubq7y8PPXr109PPfWUXn75ZW6hBgAAkqQIY4xp6kk0hmAwKK/Xq4qKCq6HAQCE6DYtt0H2s3NmWoPsB/9yst/f/C0kAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOvUK8BkZ2frkksuUfv27RUTE6Mbb7xRxcXFITWHDh1Senq6OnbsqHbt2mnEiBEqLS0NqSkpKVFaWpratGmjmJgYTZkyRYcPHw6pWb16tQYMGCC3262ePXsqJyfn1DoEAABhp14BZs2aNUpPT9e6deuUl5en6upqDR06VAcOHHBqJk+erHfeeUdLlizRmjVrtHv3bt10003OeE1NjdLS0lRVVaWPPvpI8+bNU05OjmbMmOHU7NixQ2lpabr66qtVVFSkSZMm6Z577tGKFSsaoGUAAGC7CGOMOdWNv/32W8XExGjNmjUaPHiwKioqdPbZZ2vBggW6+eabJUmfffaZ+vTpo4KCAg0aNEjLli3Ttddeq927dys2NlaSNHfuXGVmZurbb7+Vy+VSZmamcnNztXXrVudYI0eOVHl5uZYvX35ScwsGg/J6vaqoqJDH4znVFgEAYajbtNwG2c/OmWkNsh/8y8l+f5/WNTAVFRWSpA4dOkiSCgsLVV1drZSUFKemd+/e6tKliwoKCiRJBQUFSkpKcsKLJKWmpioYDGrbtm1OzZH7qKup2wcAADiztTzVDWtrazVp0iRdfvnluvDCCyVJgUBALpdL0dHRIbWxsbEKBAJOzZHhpW68buxENcFgUAcPHlRUVNRR86msrFRlZaXzOhgMnmprAACgmTvlMzDp6enaunWrFi5c2JDzOWXZ2dnyer3OkpCQ0NRTAgAAjeSUAszEiRO1dOlSvf/+++rcubOz3ufzqaqqSuXl5SH1paWl8vl8Ts2P70qqe/1TNR6P55hnXyQpKytLFRUVzrJr165TaQ0AAFigXgHGGKOJEyfqzTff1KpVq9S9e/eQ8YEDB6pVq1bKz8931hUXF6ukpER+v1+S5Pf7tWXLFpWVlTk1eXl58ng8SkxMdGqO3EddTd0+jsXtdsvj8YQsAAAgPNXrGpj09HQtWLBAf/7zn9W+fXvnmhWv16uoqCh5vV6NGzdOGRkZ6tChgzwej+6//375/X4NGjRIkjR06FAlJiZq9OjRmjVrlgKBgKZPn6709HS53W5J0oQJE/T8889r6tSpuvvuu7Vq1SotXrxYubkNc9U4AACwW73OwMyZM0cVFRW66qqrFBcX5yyLFi1yap5++mlde+21GjFihAYPHiyfz6c33njDGY+MjNTSpUsVGRkpv9+vO+64Q2PGjNGjjz7q1HTv3l25ubnKy8tTv3799NRTT+nll19WampqA7QMAABsd1rPgWnOeA4MAOB4eA5M8/WzPAcGAACgKRBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxT7wCzdu1aXXfddYqPj1dERITeeuutkPE777xTERERIcuwYcNCavbu3atRo0bJ4/EoOjpa48aN0/79+0NqNm/erCuvvFKtW7dWQkKCZs2aVf/uAABAWKp3gDlw4ID69eunF1544bg1w4YN0549e5zl9ddfDxkfNWqUtm3bpry8PC1dulRr167V+PHjnfFgMKihQ4eqa9euKiws1BNPPKGHH35YL730Un2nCwAAwlDL+m4wfPhwDR8+/IQ1brdbPp/vmGOffvqpli9fro8//lgXX3yxJOm5557TNddcoyeffFLx8fGaP3++qqqq9Mc//lEul0sXXHCBioqK9Pvf/z4k6AAAgDNTo1wDs3r1asXExKhXr16677779N133zljBQUFio6OdsKLJKWkpKhFixZav369UzN48GC5XC6nJjU1VcXFxfr++++PeczKykoFg8GQBQAAhKcGDzDDhg3Tn/70J+Xn5+u///u/tWbNGg0fPlw1NTWSpEAgoJiYmJBtWrZsqQ4dOigQCDg1sbGxITV1r+tqfiw7O1ter9dZEhISGro1AADQTNT7J6SfMnLkSOefk5KS1LdvX/Xo0UOrV6/WkCFDGvpwjqysLGVkZDivg8EgIQYAgDDV6LdRn3vuuerUqZO++OILSZLP51NZWVlIzeHDh7V3717nuhmfz6fS0tKQmrrXx7u2xu12y+PxhCwAACA8NXqA+eabb/Tdd98pLi5OkuT3+1VeXq7CwkKnZtWqVaqtrVVycrJTs3btWlVXVzs1eXl56tWrl84666zGnjIAAGjm6h1g9u/fr6KiIhUVFUmSduzYoaKiIpWUlGj//v2aMmWK1q1bp507dyo/P1833HCDevbsqdTUVElSnz59NGzYMN17773asGGDPvzwQ02cOFEjR45UfHy8JOn222+Xy+XSuHHjtG3bNi1atEizZ88O+YkIAACcueodYDZu3Kj+/furf//+kqSMjAz1799fM2bMUGRkpDZv3qzrr79e559/vsaNG6eBAwfqL3/5i9xut7OP+fPnq3fv3hoyZIiuueYaXXHFFSHPePF6vXrvvfe0Y8cODRw4UA888IBmzJjBLdQAAECSFGGMMU09icYQDAbl9XpVUVHB9TAAgBDdpuU2yH52zkxrkP3gX072+5u/hQQAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgnXoHmLVr1+q6665TfHy8IiIi9NZbb4WMG2M0Y8YMxcXFKSoqSikpKfr8889Davbu3atRo0bJ4/EoOjpa48aN0/79+0NqNm/erCuvvFKtW7dWQkKCZs2aVf/uAABAWKp3gDlw4ID69eunF1544Zjjs2bN0rPPPqu5c+dq/fr1atu2rVJTU3Xo0CGnZtSoUdq2bZvy8vK0dOlSrV27VuPHj3fGg8Gghg4dqq5du6qwsFBPPPGEHn74Yb300kun0CIAAAg3EcYYc8obR0TozTff1I033ijpn2df4uPj9cADD+g3v/mNJKmiokKxsbHKycnRyJEj9emnnyoxMVEff/yxLr74YknS8uXLdc011+ibb75RfHy85syZowcffFCBQEAul0uSNG3aNL311lv67LPPTmpuwWBQXq9XFRUV8ng8p9oiACAMdZuW2yD72TkzrUH2g3852e/vBr0GZseOHQoEAkpJSXHWeb1eJScnq6CgQJJUUFCg6OhoJ7xIUkpKilq0aKH169c7NYMHD3bCiySlpqaquLhY33///TGPXVlZqWAwGLIAAIDw1KABJhAISJJiY2ND1sfGxjpjgUBAMTExIeMtW7ZUhw4dQmqOtY8jj/Fj2dnZ8nq9zpKQkHD6DQEAgGYpbO5CysrKUkVFhbPs2rWrqacEAAAaSYMGGJ/PJ0kqLS0NWV9aWuqM+Xw+lZWVhYwfPnxYe/fuDak51j6OPMaPud1ueTyekAUAAISnBg0w3bt3l8/nU35+vrMuGAxq/fr18vv9kiS/36/y8nIVFhY6NatWrVJtba2Sk5OdmrVr16q6utqpycvLU69evXTWWWc15JQBAICF6h1g9u/fr6KiIhUVFUn654W7RUVFKikpUUREhCZNmqT/+q//0ttvv60tW7ZozJgxio+Pd+5U6tOnj4YNG6Z7771XGzZs0IcffqiJEydq5MiRio+PlyTdfvvtcrlcGjdunLZt26ZFixZp9uzZysjIaLDGAQCAvVrWd4ONGzfq6quvdl7XhYqxY8cqJydHU6dO1YEDBzR+/HiVl5friiuu0PLly9W6dWtnm/nz52vixIkaMmSIWrRooREjRujZZ591xr1er9577z2lp6dr4MCB6tSpk2bMmBHyrBgAAHDmOq3nwDRnPAcGAHA8PAem+WqS58AAAAD8HAgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA69f5bSAAANGcN9WcC0LxxBgYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6LZt6AgAA2KrbtNyfrNk5M+1nmMmZhzMwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACs0+AB5uGHH1ZERETI0rt3b2f80KFDSk9PV8eOHdWuXTuNGDFCpaWlIfsoKSlRWlqa2rRpo5iYGE2ZMkWHDx9u6KkCAABLNcpt1BdccIFWrlz5r4O0/NdhJk+erNzcXC1ZskRer1cTJ07UTTfdpA8//FCSVFNTo7S0NPl8Pn300Ufas2ePxowZo1atWunxxx9vjOkCAADLNEqAadmypXw+31HrKyoq9Morr2jBggX6xS9+IUl69dVX1adPH61bt06DBg3Se++9p+3bt2vlypWKjY3VRRddpMcee0yZmZl6+OGH5XK5GmPKAADAIo1yDcznn3+u+Ph4nXvuuRo1apRKSkokSYWFhaqurlZKSopT27t3b3Xp0kUFBQWSpIKCAiUlJSk2NtapSU1NVTAY1LZt2457zMrKSgWDwZAFAACEpwYPMMnJycrJydHy5cs1Z84c7dixQ1deeaX27dunQCAgl8ul6OjokG1iY2MVCAQkSYFAICS81I3XjR1Pdna2vF6vsyQkJDRsYwAAoNlo8J+Qhg8f7vxz3759lZycrK5du2rx4sWKiopq6MM5srKylJGR4bwOBoOEGAAAwlSj30YdHR2t888/X1988YV8Pp+qqqpUXl4eUlNaWupcM+Pz+Y66K6nu9bGuq6njdrvl8XhCFgAAEJ4aPcDs379fX375peLi4jRw4EC1atVK+fn5znhxcbFKSkrk9/slSX6/X1u2bFFZWZlTk5eXJ4/Ho8TExMaeLgAAsECD/4T0m9/8Rtddd526du2q3bt366GHHlJkZKRuu+02eb1ejRs3ThkZGerQoYM8Ho/uv/9++f1+DRo0SJI0dOhQJSYmavTo0Zo1a5YCgYCmT5+u9PR0ud3uhp4uAACwUIMHmG+++Ua33XabvvvuO5199tm64oortG7dOp199tmSpKefflotWrTQiBEjVFlZqdTUVL344ovO9pGRkVq6dKnuu+8++f1+tW3bVmPHjtWjjz7a0FMFAACWijDGmKaeRGMIBoPyer2qqKjgehgAOIN0m5bb1FMIsXNmWlNPwSon+/3N30ICAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACs07KpJwAAwMnqNi23qaeAZoIzMAAAwDoEGAAAYB1+QgIAoBGdzM9eO2em/QwzCS+cgQEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA63UQMAmgWesov64AwMAACwDmdgAABoYjzsrv44AwMAAKzTrAPMCy+8oG7duql169ZKTk7Whg0bmnpKAACgGWi2AWbRokXKyMjQQw89pL/+9a/q16+fUlNTVVZW1tRTAwAATSzCGGOaehLHkpycrEsuuUTPP/+8JKm2tlYJCQm6//77NW3atJ/cPhgMyuv1qqKiQh6Pp7GnCwBhiTuD7BIO18mc7Pd3s7yIt6qqSoWFhcrKynLWtWjRQikpKSooKDjmNpWVlaqsrHReV1RUSPrnvwgAwKmprfyhqaeAeugyeclP1mx9JPVnmMmpq/ve/qnzK80ywPzjH/9QTU2NYmNjQ9bHxsbqs88+O+Y22dnZeuSRR45an5CQ0ChzBADARt5nmnoGJ2ffvn3yer3HHW+WAeZUZGVlKSMjw3ldW1urvXv3qmPHjoqIiGiQYwSDQSUkJGjXrl1n3M9S9H5m9i6d2f3T+5nZu3Rm99/UvRtjtG/fPsXHx5+wrlkGmE6dOikyMlKlpaUh60tLS+Xz+Y65jdvtltvtDlkXHR3dKPPzeDxn3H/Qdej9zOxdOrP7p/czs3fpzO6/KXs/0ZmXOs3yLiSXy6WBAwcqPz/fWVdbW6v8/Hz5/f4mnBkAAGgOmuUZGEnKyMjQ2LFjdfHFF+vSSy/VM888owMHDuiuu+5q6qkBAIAm1mwDzK233qpvv/1WM2bMUCAQ0EUXXaTly5cfdWHvz8ntduuhhx466qeqMwG9n5m9S2d2//R+ZvYundn929J7s30ODAAAwPE0y2tgAAAAToQAAwAArEOAAQAA1iHAAAAA64R9gPn73/+uO+64Qx07dlRUVJSSkpK0ceNGZ9wYoxkzZiguLk5RUVFKSUnR559/HrKPvXv3atSoUfJ4PIqOjta4ceO0f//+kJrNmzfryiuvVOvWrZWQkKBZs2YdNZclS5aod+/eat26tZKSkvTuu+82TtOSunXrpoiIiKOW9PR0SdKhQ4eUnp6ujh07ql27dhoxYsRRDw4sKSlRWlqa2rRpo5iYGE2ZMkWHDx8OqVm9erUGDBggt9utnj17Kicn56i5vPDCC+rWrZtat26t5ORkbdiwodH6lqSamhr99re/Vffu3RUVFaUePXroscceC/m7GuH6vtfZt2+fJk2apK5duyoqKkqXXXaZPv74Y2c8XPpfu3atrrvuOsXHxysiIkJvvfVWyHhz6vNk5tLQ/b/xxhsaOnSo80TyoqKio/Zh62fBiXqvrq5WZmamkpKS1LZtW8XHx2vMmDHavXt3yD5sfe9/6n1/+OGH1bt3b7Vt21ZnnXWWUlJStH79+rDo/cc7D1t79+41Xbt2NXfeeadZv369+eqrr8yKFSvMF1984dTMnDnTeL1e89Zbb5lPPvnEXH/99aZ79+7m4MGDTs2wYcNMv379zLp168xf/vIX07NnT3Pbbbc54xUVFSY2NtaMGjXKbN261bz++usmKirK/M///I9T8+GHH5rIyEgza9Yss337djN9+nTTqlUrs2XLlkbpvayszOzZs8dZ8vLyjCTz/vvvG2OMmTBhgklISDD5+flm48aNZtCgQeayyy5ztj98+LC58MILTUpKitm0aZN59913TadOnUxWVpZT89VXX5k2bdqYjIwMs337dvPcc8+ZyMhIs3z5cqdm4cKFxuVymT/+8Y9m27Zt5t577zXR0dGmtLS0Ufo2xpjf/e53pmPHjmbp0qVmx44dZsmSJaZdu3Zm9uzZTk24vu91brnlFpOYmGjWrFljPv/8c/PQQw8Zj8djvvnmm7Dq/9133zUPPvigeeONN4wk8+abb4aMN6c+T2YuDd3/n/70J/PII4+YP/zhD0aS2bRp01H7sPWz4ES9l5eXm5SUFLNo0SLz2WefmYKCAnPppZeagQMHhuzD1vf+p973+fPnm7y8PPPll1+arVu3mnHjxhmPx2PKysqs7/1IYR1gMjMzzRVXXHHc8draWuPz+cwTTzzhrCsvLzdut9u8/vrrxhhjtm/fbiSZjz/+2KlZtmyZiYiIMH//+9+NMca8+OKL5qyzzjKVlZUhx+7Vq5fz+pZbbjFpaWkhx09OTjb//u//fnpNnqRf//rXpkePHqa2ttaUl5ebVq1amSVLljjjn376qZFkCgoKjDH//B+kRYsWJhAIODVz5swxHo/H6XPq1KnmggsuCDnOrbfealJTU53Xl156qUlPT3de19TUmPj4eJOdnd0ofRpjTFpamrn77rtD1t10001m1KhRxpjwf99/+OEHExkZaZYuXRqyfsCAAebBBx8M2/5//EHenPo8mbmcrmN9kdXZsWPHMQNMuHwWnKj3Ohs2bDCSzNdff22MCZ/3/mR6r6ioMJLMypUrjTHh03tY/4T09ttv6+KLL9a//du/KSYmRv3799cf/vAHZ3zHjh0KBAJKSUlx1nm9XiUnJ6ugoECSVFBQoOjoaF188cVOTUpKilq0aOGckisoKNDgwYPlcrmcmtTUVBUXF+v77793ao48Tl1N3XEaU1VVlV577TXdfffdioiIUGFhoaqrq0Pm07t3b3Xp0iWk76SkpJAHB6ampioYDGrbtm0n1VNVVZUKCwtDalq0aKGUlJRG7fuyyy5Tfn6+/va3v0mSPvnkE33wwQcaPny4pPB/3w8fPqyamhq1bt06ZH1UVJQ++OCDsO+/TnPq82Tm0hTC/bPgSBUVFYqIiHD+Rt6Z8t5XVVXppZdektfrVb9+/Zw5h0PvYR1gvvrqK82ZM0fnnXeeVqxYofvuu0//+Z//qXnz5kmSAoGAJB31dN/Y2FhnLBAIKCYmJmS8ZcuW6tChQ0jNsfZx5DGOV1M33pjeeustlZeX684773Tm4nK5jvpjlz/u+1R7CgaDOnjwoP7xj3+opqbmZ+972rRpGjlypHr37q1WrVqpf//+mjRpkkaNGhUy/3B939u3by+/36/HHntMu3fvVk1NjV577TUVFBRoz549Yd9/nebU58nMpSmE+2dBnUOHDikzM1O33Xab88cJw/29X7p0qdq1a6fWrVvr6aefVl5enjp16uTMKRx6D+sAU1tbqwEDBujxxx9X//79NX78eN17772aO3duU0/tZ/XKK69o+PDhP/mnycPF4sWLNX/+fC1YsEB//etfNW/ePD355JNOcD0T/O///q+MMTrnnHPkdrv17LPP6rbbblOLFmH9vzxwlOrqat1yyy0yxmjOnDlNPZ2fzdVXX62ioiJ99NFHGjZsmG655RaVlZU19bQaVFh/msXFxSkxMTFkXZ8+fVRSUiJJ8vl8knTUFfelpaXOmM/nO+pNP3z4sPbu3RtSc6x9HHmM49XUjTeWr7/+WitXrtQ999zjrPP5fKqqqlJ5eflx53M6PXk8HkVFRalTp06KjIz82fueMmWKcxYmKSlJo0eP1uTJk5WdnR0y/3B+33v06KE1a9Zo//792rVrlzZs2KDq6mqde+65Z0T/R86hOfR5MnNpCuH+WVAXXr7++mvl5eU5Z1/q5hzO733btm3Vs2dPDRo0SK+88opatmypV155xZlTOPQe1gHm8ssvV3Fxcci6v/3tb+rataskqXv37vL5fMrPz3fGg8Gg1q9fL7/fL0ny+/0qLy9XYWGhU7Nq1SrV1tYqOTnZqVm7dq2qq6udmry8PPXq1UtnnXWWU3Pkcepq6o7TWF599VXFxMQoLS3NWTdw4EC1atUqZD7FxcUqKSkJ6XvLli0h/5HXfQDUhcKf6snlcmngwIEhNbW1tcrPz2/Uvn/44YejzjRERkaqtrZW0pnxvtdp27at4uLi9P3332vFihW64YYbzpj+m1OfJzOXphDOnwV14eXzzz/XypUr1bFjx5DxM+29r62tVWVlpTPnsOj9tC8DbsY2bNhgWrZsaX73u9+Zzz//3MyfP9+0adPGvPbaa07NzJkzTXR0tPnzn/9sNm/ebG644YZj3mbZv39/s379evPBBx+Y8847L+R2s/LychMbG2tGjx5ttm7dahYuXGjatGlz1O1mLVu2NE8++aT59NNPzUMPPdTot9PW1NSYLl26mMzMzKPGJkyYYLp06WJWrVplNm7caPx+v/H7/c543a2TQ4cONUVFRWb58uXm7LPPPuatk1OmTDGffvqpeeGFF45566Tb7TY5OTlm+/btZvz48SY6OjrkjoaGNnbsWHPOOec4t1G/8cYbplOnTmbq1KlOTTi/78YYs3z5crNs2TLz1Vdfmffee8/069fPJCcnm6qqqrDqf9++fWbTpk1m06ZNRpL5/e9/bzZt2uTcadKc+jyZuTR0/999953ZtGmTyc3NNZLMwoULzaZNm8yePXucfdj6WXCi3quqqsz1119vOnfubIqKikIeKXHkXTW2vvcn6n3//v0mKyvLFBQUmJ07d5qNGzeau+66y7jdbrN161brez9SWAcYY4x55513zIUXXmjcbrfp3bu3eemll0LGa2trzW9/+1sTGxtr3G63GTJkiCkuLg6p+e6778xtt91m2rVrZzwej7nrrrvMvn37Qmo++eQTc8UVVxi3223OOeccM3PmzKPmsnjxYnP++ecbl8tlLrjgApObm9vwDR9hxYoVRtJR/RhjzMGDB81//Md/mLPOOsu0adPG/OpXvwr5UDPGmJ07d5rhw4ebqKgo06lTJ/PAAw+Y6urqkJr333/fXHTRRcblcplzzz3XvPrqq0cd67nnnjNdunQxLpfLXHrppWbdunUN2uePBYNB8+tf/9p06dLFtG7d2px77rnmwQcfDPngCuf33RhjFi1aZM4991zjcrmMz+cz6enppry83BkPl/7ff/99I+moZezYsc2uz5OZS0P3/+qrrx5z/KGHHnL2YetnwYl6r7tt/FhL3bOwjLH3vT9R7wcPHjS/+tWvTHx8vHG5XCYuLs5cf/31ZsOGDSH7sLX3I0UYc8TjSQEAACwQ1tfAAACA8ESAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1/h8YO3mhBkIMugAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.00552 0.0995\n"
     ]
    }
   ],
   "source": [
    "#Restart - may want to comment out first line\n",
    "#HDunitList = [latestHDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()\n",
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "27b51cd0-8bb6-4b16-bb79-37498e59ce5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 621 HDs with pop < 117994 to within 1080\n",
      "working on squaring up HD 9 time is now 0\n",
      "working on squaring up HD 1000 time is now 1\n",
      "working on squaring up HD 2599 time is now 2\n",
      "working on squaring up HD 3717 time is now 5\n",
      "working on squaring up HD 5394 time is now 6\n",
      "working on squaring up HD 7115 time is now 6\n",
      "working on squaring up HD 8570 time is now 7\n",
      "We have addressed underpop in a total of 621 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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+V1tVrUuwFCmtnQysSHWNDoHC0nJ8ZL2EwtJyw58kRPSdSBrdRa0p+V6lrkEpWIqU1k6OCiRVcaQQUXiLpNFd1Jqv798dKViKlNZOtliRajzNA9WyOZiIjEuLqt2kH96+f29s9tqIae1kYEWqiJS+cyIKfdVu0hdP3783FdfrImbWEXYFkiqU9J37U4yOiPQl0KrdZGzS9//6X0/jd38r9bl8aoI5YmYdYYsVqSJS+s6J6DuswxcZPA1Iio4y4e4+HWStw5LUFkBktHayxYpUESl950REkcTXgCSpe89bj0XL7r1wb+1kixWpIlL6zomIIoWcAUlS9563a7+77r3oKBOG90hFx/ZxuHytKU0kXHJw2WJFqoiUvnMiokjga0CSCU0Dku7rb3F27ykptRPOpXk4CbMPWk3iaFThfLIQEUWKwtJy5Kw54HO5DdNHOgckSUVAfXXvhWoSb63u32yxIlWFe985EVEk8GdAkjSYQSIlvTe/FwCQ3RJm1PsGAytSXcuTi4iIjCXQAUmeei8mf79r2JfmYWBFRERELgKZushTV5+tqhZv7Doja/tGLs3DUYFERETkovnUNe4IAPPH39qqu07OLBxypCfEKlhaX9hiRURERG4lxcegsqbB7XOLd3wOwISUBLMzj8ohhNeuPtmMmV4FgIEVERERteCpO6+5sqpaPP/+MZfH4s3Rqmz/yvU6VdajBXYFEhERkZO37jxfauobVdkHI8/SwcCKiIiInA6dq1CnO89PJgDDuqVotv1AMbAiIiIiJ61H5AkARy9c1XQfAsHAioiIiJyC2Q3XRmbUoXVwFwgGVkREROQk1bAKxsC8mw55yzHHioiIiHRBmkrmI+slFJaWo9GhLA29eQ0rtYIrEwA5M9SY0FSh3V3hUaNguQUiIqIw4WkqmQUT+iua2HjcwAysenxoq3X5SwAQMuI7AWDBhP6GnScQAExCyHmrkUur2bGJiCJdo0NwQncFPNWekj6xVY8PVRRcAa7fQXpCLGBqqjHVsX0crlbX4ZdbilF5w30B0eb+5ZYO+PuZf/hc7um7uuNXEwYo2kdPtLp/s8WKiIh0R62Wl0jhayoZE4BFH5fgvv4WRcFpdJTJ62TISfFmTPn9QZ/ruadPuqzAKqmtWfa+6RVzrIiIdCLQ3JhwIbW8tOyCslXVYsa6Y8grLtNoz/TLV+0pgaZK6YfOVai63ZE907wmuks5U1OzustKiH9z1xnDf78MrIiIdCCvuAyjlhUgZ80BzNpoRc6aAxi1rMDwNxml5Eziu+jjkogNOj2RW55A7TIG3hLdpb8XTOgPc5soLJjQX1Y1d6N/vwysiIg0xhaa72jV8mJ0cssTBKOMwbiBGVj52O1ISYhxedySFIeVjw1FUlszPrJeQlJbM2b/oI/XdYXD98scKyIiDQUrN8aotGp50RN/kval2lO2qlq3x5IJTYFOMMoY5BWXYfGOz1FR/V0Se2qCGT+8LQOLd7jmySXFyQs7jPz9MrAiItKQkhYab0nE4ULLlhc98DdpX+qSm7HuGEyAS3DVvEtO7eDc00jEiup6rPn0XKvlq2pvylqvkb9fdgUSEWmILTSufFX9DocCkp4E2iUs1Z6yJLkGJZakOL9KLfjirbU1ECnxMYb+ftliRUSkoUhvoWlJq5YXranVJTxuYAbu628JSf0vX62t/jJu2noTtlgREWkokltoPAl1y4seqJm0L9WeemhIZ2T1SgtaEBqsVtTKmgYmrxMRkX8itYXGl1C2vOiBEbuEg9mKqqf3qRRbrIiINBaJLTRyhKrlRQ+M2CXsq7U1EHp6n0qxxYqISAcirYWGXGlZLsFf3lpbA9EuNkpX71MptlgREelEJLXQkCu5Fcz1dkx4am1NTYjB7V2S/VrnqN4ddPc+lWBgRUREpANG7RIeNzADe3NHY8P0kXj6ru5ITTCjoroBRV9V+rW+3h3bqbuDIcauQCIiIp0wapdwdJQJV6vr8e6+8wGvK6tneuA7pCEGVkRERDoidQkbyc4T32DmhiJV1lV1o16V9WiFgRUREVEE8jQnodK5CvOKy/D8++oEVQCweMfnGDswQ/etdJ4ozrHas2cPJkyYgMzMTJhMJmzdutXl+c2bN2PMmDFIS0uDyWSC1WpttY7S0lI8/PDD6NChAxITEzFp0iR8++23Lst0794dJpPJ5d/SpUtdljlx4gTuvvtuxMXFoUuXLli+fHmrbW3atAn9+vVDXFwcBg0ahJ07dyp9y0RERGElr7gMo5YVIGfNAczaaEXOmgMYtawAS3aWuH28+XQ6jQ6BwtJyfGS9hH1fXMHCbSdV3Te5hVD1SnFgVV1djcGDB2PlypUenx81ahSWLVvm8fkxY8bAZDKhoKAA+/btQ319PSZMmACHw+Gy7L//+7+jrKzM+e+nP/2p8zm73Y4xY8agW7duOHr0KF577TUsXLgQ77zzjnOZ/fv3IycnB9OmTUNRUREmTpyIiRMnori4WOnbJiIiCgue5iQsq6rFf+8553WuwpYB2ZTfH4TNXqf6Phq5QKjirsD7778f999/v8fnp06dCgA4f/682+f37duH8+fPo6ioCImJiQCAP/zhD0hJSUFBQQGys7Ody7Zv3x4Wi8XtetavX4/6+nq8++67MJvNGDBgAKxWK37729/imWeeAQCsWLEC48aNw8svvwwAWLx4MfLz8/H2229j9erVSt86ERGRofkzcbK07CubP0NVTYPf9aqS42MQZTKhotp3DpWRC4SGvNxCXV0dTCYTYmNjnY/FxcUhKioKe/fudVl26dKlSEtLw+23347XXnsNN2/edD5XWFiIe+65B2az2fnY2LFjcfr0aVy9etW5TPNATVqmsLDQ6/7Z7XaXf0REROEgkImTKwMIqgDg0Tu+hwNzf4DUhBiPy4TD3JghD6xGjhyJhIQE5ObmoqamBtXV1XjppZfQ2NiIsrLv+nB/9rOfYePGjfjkk0/w7LPP4je/+Q1+8YtfOJ+32Wzo1KmTy7qlv202m9dlpOfdWbJkCZKSkpz/unTpEvB7JiIiUqp5LlNhaTkaHYHXNteyi23b8TJER5nwm4cHwQRjFUJVIuSjAjt06IBNmzZhxowZeOuttxAVFYWcnBwMHToUUVHfxXkvvvii8/+33XYbzGYznn32WSxZssSltUttc+fOddm23W5ncEVERCGVV1yGRR+XuLQuZSTFYcGE/gEVCtWyi01KSpcKobZ8fxYV3p8eaFJuYcyYMSgtLcWVK1fQpk0bJCcnw2KxoGfPnh5fM2LECNy8eRPnz59H3759YbFYWo0klP6W8rI8LeMpbwsAYmNjgxq4EREReSMll7dsn5KSyAOpwu5rTsJgk1rMjFoIVQ5Np7RJT09HcnIyCgoKcPnyZTz44IMel7VarYiKikLHjh0BAFlZWdizZw8aGhqcy+Tn56Nv375ISUlxLrN7926X9eTn5yMrKysI74aIiCgw3pLLpccWfVzid7egtzkJQ6F5i1m4zo2pOLC6fv06rFarsz7VuXPnYLVacfHiRQBARUUFrFYrSkpKAACnT5+G1Wp1yWt67733cODAAZSWlmLdunX48Y9/jDlz5qBv374AmpLO33zzTRw/fhxffvkl1q9fjzlz5uDxxx93Bk2PPfYYzGYzpk2bhpMnT+KDDz7AihUrXLrxZs2ahby8PLz++us4deoUFi5ciCNHjmDmzJn+fVpERERB5Cu5XCDwOk/eJk4OptSEGNjstarli+mVSQih6N397W9/w7333tvq8SeffBJr167F2rVr8dRTT7V6fsGCBVi4cCEA4JVXXsHatWtRUVGB7t2747nnnsOcOXNgMjVFq8eOHcPzzz+PU6dOoa6uDj169MDUqVPx4osvunTTnThxAi+88AIOHz6M9PR0/PSnP0Vubq7Ldjdt2oRXX30V58+fR58+fbB8+XI88MADst+v3W5HUlISqqqqnOUhiIgofCitNB5MH1kvYdZGq8/lVkwegoeGdA5oWy3f97BuKfiX1z7x2k3YLrYNqutuBtyNqEa+mC9a3b8VB1aRhoEVEVH4ClaSuL8KS8uRs+aAz+U2TB8ZlPkEpfwuACHJwfrdY0PxwG3B+Zy1un9rmmNFRESkFU8VyJtXGg81KbncU3tZsOs8eeomDJaZG45h54lvQrKtUGFgRUREESfYSeL+8pZcHqo6T+MGZuDvL98b9JwrAHAI4Pn3i8IquGJgRUREEScUSeL+8tRqZEmKC6jUghJHL1xFRXWD7wVVMnNDEXaeCH0LYTBoUseKiIhIS3IrkGtVqVzrOk+hft9NLVfHsDoqNIFjMDGwIiJd0NPILAp/ciuQa1mpXKrzpAWt3veij0twX3+Loc99BlZEpDm9jcyi8OerArkJTV1vRp4MOBBXq+s12a7U/apVQKkG5lgRkab0ODKLwp8eksT1qtEhsHhHiWbb13KiaDUwsCIizeh1ZBZFBj0kieuRr8T+YDt/pVqzbauBXYFEpBklI7OM3DVA+qV1krgSocpDlNtiZI4C6h2qbx4bDl3EzNF9dPkdyMHAiog0o/eRWRQZtEwSlyuUeYhyE9eDEVQBgM1eZ+gfU+wKJCLNGGFkFpHW/MlDbHQIFJaW4yPrJcWTHsup/h7sxiQj/5hiixURaYYjs4i885WHaELrEgV5xWVYuO0kbPY657KWxFgsfHCArNYtKbF/xrpjMMF1zkDp72DPMpzeLja4GwgitlgRkWY4MovIO6UV4vOKy/DcumMuQRXQ1L32nIJRtt4S+5++q7ui9+AXA49XYYsVEWlKuoC3zB+xsI4VkaI8xEaHwCubP/O63CubP5NdgNNTYv+hcxV4d995WfvlryvVdb4X0ikGVkSkOSONzCIKJSV5iAdKy1FZ431+v8qaBhwoLcddfdJlrdddYv/wHqmwJMa2ahVTk5HzKhlYEZEuGGFkFlGoDeuWgihT01x6nkSZmpZ7a/cZWess/PKK7MDKnfwSG2pvBmlIIL57P0bFHCsiIiKdOnrhqtegCmgKuo5euIrWmYqe+N8SLI1Q9NUyFojv3o8xMbAiIiLSKSU5VnJbfP1tGfY2QlFtLLdAREREqlOSYzW8RyqS42O8tialxMdgZE/vgZWnCu+hnOqGOVZERESkOiW13qKjTFj6yCA8t+6Yx/UteWSQ10Eh3iq832gIXl5VcxkGr13HrkAiIiKdUlrrbdzADKx+fCgsia4tPhlJcVjtY2JpbxXen1t3DK9u9V7KQS1Gr11nEiLY9VONzW63IykpCVVVVUhMTNR6d4iIKAIpnStQ6YTNjQ6BUcsKQtbV58n0u7tj3vgBqqxLq/s3uwKJiIh0TmmtN6XlS0KZP+XN7z89j2HdUg1dGJiBFRERkQE0D5aUtkj5opdReAKt5z40GgZWREREBqK0W1COQEfhxZujUVPfGNA6JNLch0YtGMzkdSIiIoPwlmA+Q8Ekyy1Jow/9bSNSu23JVnVD5TWGDgMrIiIiA/BWoFN6bNHHJWj0UKq90SFQWFqOj6yXUFha7rKct9GHclSr1FolqaiuV3V9ocSuQCIiIgPwlWAu4LkbTU734biBGVj1+NBWy2khqW2MptsPBFusiIiIDEDJ9DbNeeo+LPtnfarFH590tmCNG5iBvbmjsWH6SDx9V3e0j9Om/eX415WabFcNbLEiIiIyACXT20jkzO/3P/vO43/2nXdpwaq6UY/39p0PybyA4YYtVkREEcZbrg3pl68EcxNaTwejpD6VlAC/88Q3IZts2ZOuqfEabj0wbLEiIoogwRiqrzdq13jSk8nf74I3dp1t9bi76W0AZfWpxD/X8+pHxaio9jyRcyj0sxh3phMGVkREEULKtWnZEiG1VKzyMZecEYRr4OjufTVn8fAeldanEoDmQRUAVNQYd1QguwKJiCJAoEP1jSBYNZ605ul9SeZk34K9uaPdBo6B1qfSSqAFS7XEwIqIKAIoGapvROEaOPpKPjcB2Hj4onPZlrlz/tanSonXttzBkC7Jmm4/EOwKJCKKAP4O1TeKQGo86Znc9/V2wRfYePiixy5QufWpTACS4mM0Hw34/sELmHZ3T433wj8MrIiIIoA/Q/WNJFwDR7n7+8auM60ea5k7d19/Cw6dq0B+iQ3v7jsPE+ASQEl/V9Zon2N1oaJG613wG7sCiYgigD9D9Y0kXAPHQPa3ZRdodJQJWb3S8KsJA7D68aGwJLmuu1NiLJI17gKUdGO5BSIi0jMp12bGumNuWyqA1kP1jUQKHG1VtW67sUxoGjlntMDR1/vyxVMXaPMWLKkshUMITPn9QdX23V8mAFOzumu9G35jixURUYSQcm1atlRYkuIMX2rBW5K2kQNHOe9LDnddilIL1kNDOiOrVxquXK/zf0dVNKRLEsxtjBuesMWKiCiCuGupCJcCmp6StD3VeDIKb+/LU8HQluR0KarRTTr7B73xxwMXAqqF9WJ234D3Q0sMrIhUFs5Vnyk8SC0VchjteA7XwNHT+wKAjYe/UqULNNBuRwDo0aEdDs+7D2/kn8Hbn3zh1zqioo39XTGwIlJRuFZ9pshk1ONZSeBoJJ7el1q5c97y8OQ6f6Ua0VEm3NU73e/AylZ5w6/X6YVxOzGJdCZcqz5TZOLxbBxq5s55WpdcGw5dRKND4Gp1HfxtJLR+XenfC3WCLVZhxGhN9uHEV9VnE5qGPN/X38LvhHSPx7PxqNkFKq1r4bZi/O+Bi4pea7PX4a3dZ/DW7i8CKDJq7GOKgVWYMGqTfbgI16rPFJl4PBuTml2g0VEmDO2aojiwAhBgUAXcqL8ZwKu1x67AMMAme+2Fa9Vnikw8ngkALvpZ/TzQ6XA+PfsPw83p2BwDK4ML14lHjSZcqz5TZOLxTHnFZbLKOATDt9fqDTsZOMDAyvDCfcZ6owj36UIosvB4Dh+NDoHC0nJ8ZL2EwtJyWT+yGx0CC7edDMHeeWbk1lDmWBkcm+z1IdynC6HIwuM5PPibe/t2wRew2bWtwm7k1lC2WBkcm+z1I5ynC6HIw+PZ2PzNvW3qAjwTil30yJIYa+jWULZYGVy4TjxqVOFa9ZkiU6iOZ5aKUZe/5TKk12nt+91TDP39M7AyuGA12fNC579wrfpMkSnYxzNLxajP33IZvl4XKp+ebcoFM+o9h4FVGFB74lFe6IgoFKTuqpYtK1J3Fbsc/eNv7q1ecnErbzQYukYaA6swoVaTPS90RBQKrO6uPqmn4ey312Ut3zL3Vk+5uDa7PoI8fzCwCiOBNtnzQkdEocLq7upy19PgiafcW185u80lxEajuq7R/x32oeK6tqMSA8FRgeTEmlhEFCosFaMeTyMA3fGWeyvl7MoxMsgDolITzEFdfzAxsCInXuiIKFRYKkYd3noa3PFVLmPcwAysfOx2+OqUKPqqUtF+KmVJahvU9QcTuwLJiRc6IgoVlopRh9yRfDPv7YW7eneQlXubkhALXwXaK6obkJpgRkV1vZLdlcUEYFi3FNXXGypssSInTmNBpA/+TENiNM27nVpec1jdXT65PQh9OrVHVq80WZ+n3HWO6BGc4EcAOHrhalDWHQpssSInTmNBpL1IKneidqmYSBSMngb5ywbvXmDklBMGVuSCFzoi7URiuRPOVhCYYHSpyh0d+Odim9Ldlc3IKScmIUT4tTGryG63IykpCVVVVUhMTNR6d0ImGJXXWc2dyLNGh8CoZQUe82WkG+Te3NE8b8JQINdHKSAH3Pc0tAzI5WzLU5DfUsveDTVEmYBTi++HuU1g2Upa3b/ZYkVuqT2NRSR1bxD5g3WdIleg10clPQ1ytzVuYAZmZ9/ic0LmYLTMOERTjpVRj3MGVhR0kdi9QaQUy51EJm/Xx+fWHcOc7D7onp7gsxVLTpeq0mtx9/R4ld6lckY+zhlYUVCxmjuRPCx3Enl8XR8B4I1dZ52P+WrF8tbTIGdbuf93Au3jYjCyZ9PoQS2PNSMf5yy3QEHFau5E8rDcSeSRW4NKIrUs5RWXBWVbVbU3MeX3BzFqWQHyist8HpPBkhjXxtDHueLAas+ePZgwYQIyMzNhMpmwdetWl+c3b96MMWPGIC0tDSaTCVartdU6SktL8fDDD6NDhw5ITEzEpEmT8O2337osU1FRgSlTpiAxMRHJycmYNm0arl93nVjyxIkTuPvuuxEXF4cuXbpg+fLlrba1adMm9OvXD3FxcRg0aBB27typ9C1TANi9QSQP6zpFHqXXPalladHHJYprmynZVtk/A7j8EpvHYzKYfjT0e4Y+zhUHVtXV1Rg8eDBWrlzp8flRo0Zh2bJlHp8fM2YMTCYTCgoKsG/fPtTX12PChAlwOBzO5aZMmYKTJ08iPz8f27dvx549e/DMM884n7fb7RgzZgy6deuGo0eP4rXXXsPChQvxzjvvOJfZv38/cnJyMG3aNBQVFWHixImYOHEiiouLlb5t8hO7N4jkk5KQLUmu54OvaUhIPj0VX/XnuudvK7/SbQl8l6bh7pgMpjEDLCHbVjAEVG7BZDJhy5YtmDhxYqvnzp8/jx49eqCoqAhDhgxxPv7Xv/4V999/P65eveoc/lhVVYWUlBT89a9/RXZ2Nj7//HP0798fhw8fxh133AEAyMvLwwMPPICvv/4amZmZWLVqFebNmwebzQazuWmyxldeeQVbt27FqVOnAACPPvooqqursX37duf2R44ciSFDhmD16tWy3mOklltQizSE3FeNFQ4hJ/oOS5MEh95GJ/u6PnqzYvIQPDSks+JtKel6BIAN00ciq1eayzF56Fw51h/8SuEey6NWqQVAu/t3yHOs6urqYDKZEBsb63wsLi4OUVFR2Lt3LwCgsLAQycnJzqAKALKzsxEVFYWDBw86l7nnnnucQRUAjB07FqdPn8bVq1edy2RnZ7tsf+zYsSgsLPS6f3a73eUf+Y/dG0TKSUnIDw3pLHsaEvJOGhHXMrAIJG8pUM2vj0opbYHyd1tSF6J0TMa2icL2E8ErDCqVWjCykAdWI0eOREJCAnJzc1FTU4Pq6mq89NJLaGxsRFlZ04Fts9nQsWNHl9e1adMGqampsNlszmU6derksoz0t69lpOfdWbJkCZKSkpz/unTpEtgbVpmemrHlYvcGEWlJzog4f/KW1CBdH1MTYmS/xt9BDOMGZmBOdh9Fr2kewEnBadWNBsXbVsLoObchL7fQoUMHbNq0CTNmzMBbb72FqKgo5OTkYOjQoYiK0n6Q4ty5c/Hiiy86/7bb7boJrvTWjK0Ep60golBp2ZXqEELXxVfHDczA6H6dMHLJblRU13td1oTAWvlnju6DDYe+gs3uO3hpHsB5C07VZvScW03qWI0ZMwalpaW4cuUK2rRpg+TkZFgsFvTs2RMAYLFYcPnyZZfX3Lx5ExUVFbBYLM5lWo4klP72tYz0vDuxsbEu3ZR6EQ5FNtWu5k6kBiPnMxl534PF3Q/Q5LbyWoO0bCkxt4nCbx4e6HZqGklKfAyWPDIooGt9dJQJCx/s73O6mpYB3IHScsX5Wf5IiY8xdKkFQOMCoenp6QCAgoICXL58GQ8++CAAICsrC5WVlTh69CiGDRvmXMbhcGDEiBHOZebNm4eGhgbExDSdNPn5+ejbty9SUlKcy+zevRuzZ892bjM/Px9ZWVmheouqYJFNouAwciuwkfc9WDz9AK2U2XWldUuJp6lpktvG4Km7umPm6D6qXOM9bUfS8jjKKy7DK3/6LODtyhEO0xcrDqyuX7+OL774wvn3uXPnYLVakZqaiq5du6KiogIXL17EN998AwA4ffo0gKbWI6ml6L333sOtt96KDh06oLCwELNmzcKcOXPQt29fAMCtt96KcePGYfr06Vi9ejUaGhowc+ZMTJ48GZmZmQCAxx57DIsWLcK0adOQm5uL4uJirFixAm+88YZz32bNmoV/+Zd/weuvv47x48dj48aNOHLkiEtJBiPgHGJE6gtVK3AwWpWM2IId7Na1QLqqpNHJemgpCVXaRPPt2Oy1qLheh9QEMyxJbV22J3cyZrVU3rhp+HuZ4sDqyJEjuPfee51/S/lITz75JNauXYtt27bhqaeecj4/efJkAMCCBQuwcOFCAE3B1ty5c1FRUYHu3btj3rx5mDNnjst21q9fj5kzZ+IHP/gBoqKi8KMf/QhvvfWW8/mkpCT89a9/xQsvvIBhw4YhPT0dv/rVr1xqXd155514//338eqrr+KXv/wl+vTpg61bt2LgwIFK37amWGSTwo3WXVihagUORquSEVuwQ9G6prSKuUSPo5NDlTbhazuhzKtqzuj3soDqWEUCPdSxKiwtR86aAz6Xk+qNEOmZHrqwQnFOefqlL926/W1VMtr1IFifQ0sfWS9h1karz+WS28a4dA1GavepnB83co81tal17Gp1/+YkzAYgzdfkq8imHpqxibzRSxdWsFuBg9mqZKQW7FC2rsnNj1r52FBERZkiOuFf7o8bLY6hxLhow9/LtK9vQD75U2TTiPWuKLzpqZZQsKdaCubk40aaJiqUk7DLncR6ZK+0iC6+qqRQqhbH0BgddWH7i4GVQSgpsplXXIZRywqQs+YAZm20ImfNAeds5URaCeVN1he5N2F/fzkHs1Up2PuuplC2rnGWB998/bgRAOZtKUb9zaZ5e30da8FwV+/0EG4tOBhYGci4gRnYmzsaG6aPxIrJQ7Bh+kjszR3dKqjS27QNRIC+urCCfRMOZquSkQKIULeucZYH7+Qk+JdX12Pkkl3IKy7zaxqcp+7showAJmy2JLX1+7V6wRwrg/E2isOIo4UocuitC8tTLR+LCsnMwc6LDOa+q0mL/NBImeXBn5G1cn+0VFQ3uOQ8rnp8KH65pdhnVXgAyL7VguxbLSj88gocAth46CtU1Ph+HQCkJZh10dIaKAZWYYT1rkjP9DgII1g3YemX/ox1x2CCaxVtpa1Knm6gRggg1PwclG5Xz9e4QMuN+DuyVumPFumH+HdT7uxCRbXnYqvJ8TH4+abjLtPlKPlmHxqSqavj118MrMKInrpaiFrS6iYrZ7+CcRO+r78Fs7P74L19512G9ytpVfJ1A9V7AAEYp3UtVAItNxLIyFpfP26aa/lD3NwmCv/x0EA8/36Rx9dU1jQAcA28lAxFSWprVrC0frGOlQ96qGMll9Hq21Bk0kMdq2DzNF/dU3f1wMzRvWUFj6Gq/xQqWheF1YNAv9NGh8CoZQUeeyakVt+9uaM9frZKK6m//m+3ofJGAz49+w8cvXgV12obWy1jSYxF7U3HPwMr/2X42HelWMeKAqbHrhailozQhRUITzeuqhsNeHPXGfS1tPMZEIVjvqQRWteCSY3vVK10j6T4GNlB0Et/OgFfzS+Tv98Vb+4+K2t93oRLqgpHBYYRI40Wosgm3WTDrZaQWrW69FSagtShxncaaLqHFPQraVnyFVSZAKzdf172+nzJL7Gpti6tMLAKMxxuTKQdtQIi5kuGHzW+00BG1gZr3j8BuOQQBuoj6zeGL2jNrsAwFO5dLUR6pVZApLfSFBQ4Nb7TQNI9lExS3T4u2m0ulTfx5mjU1Ct7jTvl1fWG7w5ki1WYCteuFiI9UysgMlJ1dZJHje80kHQPuUH/zHt7YeKQ78latrkHBloUv8YTo7fEMrAiIlKJWgER8yXDj1rfqb/pHuf+US1rP9tERaF7WrysZSUZSXFYPHEQTCodjkZviWVgRUSkEjUDIuZLhh+1vtOW05utnzYC//njwai76UBhaXmrHKVGh8AfD1yQte4/HriAx0Z0g5KYffL3u8L6VaXPRHc5wqElljlWRGRIeq2LpGZBTOZLhh9P3ynQVItQ7vcspXvkFZfhpf877rUu3KFzFbKmowGAiup6WL+qxAODMrD9hLy5Zbunx2OXSqP5hnVLMfzxzcAqQun1pkQkh96LjKoZEAVa/4nnuv60/E79PZ7lVmFXmrN0+Vot7uvfSXZgld4uFluslxRtw5OCU5fR6BCGPkYZWEUgvd+UiLwJZEqPUNJDQUye6/rn7/GspOCo0pylju3j4JBZ8iAlvg0g4HUOQSVq6hvxdsEXmJXdR5X1aYE5VhFGOolbDruVTuK8Ynm/UIi0oFYBzkjAc13/AjmeldRMkwZVyOHMcZLZYPRkVndcqa6Tt7BM7+0/Z+hzmIFVBOFNiYyOFcnl4bluDIEcz0pqpkmDKnzFSiZ8N7jiynV5wVKPDu1UH8VXWdNg6HOYgVUE4U2JjI4VyeXhuW4MgRzPSmumSYMqPLVcZbQYmSh3/Veu1cFmr0VcjLrhhJHPYeZYRRDelMjoWJFcHp7rxhDI8SynCntqghm2qhsoLC3H8B6pLoMqbFU3UFFdj9R2sbAkth7U4Gv9ABBlAhbv+FzWe1DKyOcwA6sIwpsSGV0gU3pEEp7rxhDI8Sx1781YdwwmoNXrBZqmh5nz4XEAroMW5Ayq8LV+AAhWT3JyfIyhz2F2BUYQTpNBRseK5PLwXDeGQI9nTwVH3fFn0IKn9Qf79Hrqzh6GPodNQqhRKzV82e12JCUloaqqComJiVrvTsCkkUKA6y8Q6RDWy1B1Im9YRsA3nuvGEejxLNUqs9lrsXj7SY+lD6QWsL25oxUFLvU3HfjfwvO4UFEDIQT+98BF2a9VKsEchRMLx6kSWGl1/2Zg5UO4BVYAb0oUHlj40jee68ahxvFcWFqOnDUHfC63YfpI2TXW3B1DwfTAwE743eN3qLIure7fzLGKQJwmg8KBHgpw6h3PdeNQ43hWe9CCp+KlwdSzQ/sQbi04GFhFKN6UiNSj59YznuuRQ81BC95qoQVTOByrDKyIiALA7jbSCzVHzfqqhRYMyW3bYGRP4wdWHBVIROQnThtDeqLmqNn8Epu6OyfD3X3SddPSGwgGVkREfuC0MaRHnkokWP5ZWf2+/hYUlpbjI+slFJaWuz0+84rL8O6+8yHa4+98fMIWFj9G2BVIROQHJdPGhEPeCBmHp0EL+SU2jFpW4LXbWvrBoJVXNn+G+/pbDN1yxRYrIiI/cNoY0jNp0MJDQzojq1ca8ktssrqttcitaq6ypgEHSss1274aGFiRbjQ6hM8maiK94LQxZBRKuq1tVTdCuWtuFX55RetdCAi7AkkXOLKKjIbzFpK/Ql2eQ2639dsFX+APheeDth/yGbcbEGBgRSoI9CLhqQid1ETNqTdIj7xNUmukeQv1XIMrHGnxI1Jud/Qbu84EZftKGT0nkYEVBUSNOa68NVGb0NREbfRkRlKPngIBaQRWy3MgJSEGDw/pjKS2ZjQ6hG6PXbYUh5ZWPyKN1B2dEh9j+FpWnCvQh3CcK1Atni4SSiZ5DcbcVhR8WgU3eg0EpM8jv8SGrdZvUFFdr6v9c0eN85fka3SIViPympM7QbKnc8/bOSlt21O3tZ6sVvG441yBpHvNT9z0drFYuO1kwC1Neh9ZpafWEb3QKrjRc5dxdJQJVTfq8d6+87rcv5bYUhx6apTn8HTuPTg4A9uOl3k8J311W+sh2EpNMOM3Dw/UzTkSCAZWJIvSGc7l1vDR88gqvbaOaEmr4EbvgYAa+xfKIJ41uEJP7o/DP/+z7EHL79/TuVdWVYv/3nOu1XpanpOeuq0tSXGY/P0ueGPXWeVvSkUPDs4Im+sqAyvyKZAZzn1dTPQ6skrPrSNa0TK40XsgEOj+hTqI13tLcTiS++Pwj4UX8MfCCy7fvz8TIrs7Jz0VDgWAjYe/0rSr8OPjZZj/wwFh0ULKOlZhIlg1oAKd4dzXxUTNua3UwqlK3FMSPKhN74FAIPunxXyDem4pDlfSj0i5V7Lm37+/RTvdnZMtC4dGR5kQHWXC/PG3atolWF5dH5RrhxYYWIWBvOIyjFpWgJw1BzBroxU5aw5g1LICVS7I/p7QJjT94pbT0uRrbqtQtwxpGUDomZbBjd4DAX/3T6sg3tdNXsn5S/J4+xHpTvPv32YP7JzydU7mFZdh8Y7PA9qGGsKlhZRdgQYX7C4rfw50f1qaPDVRa9EsrPfWEa1oGdzotctY4u/+adXFGS41uIzGU56TJ9L3X3G9LqDtejsnA0n1UFu4tJCyxcrAQvFr158D3d+WJndN1FrQe+uIVrRs5dBjl3Fz/u6flkG83lqKI8W4gRnYmzsaG6aPxBNZ3WS9JjXBrKgbsTkTgGHdUtw+F2iqh5pMAIZ0SdZ6N1TBwMrAQtFlJfdmun7aCKyYPAQbpo/E3tzRhr4os5vEPa2DG70HAv7sn9ZBfPObfLicv0Yg/Yi8X+bnbElqq6gbsTkBYNXfSt0+p/WEy80JAHcu3R2UnMJQY1eggYXi167cLoO7+qT7vQ29YTeJZ96GbIeiDIWeuozdUbp/eujilG7yFHpKvv/oKJOibsTm3th1Bn0t7Vqdn3pLZ7ha0xAWo65Zed0HPVdeD2XV8kis6RSJ71kuFk5Vj5TjArgP4o1+kyHvlH7/zc+9T8/8A/937JKs7WS4qeou9x4SSnIr0Muh1f2bgZUPeg6sfE1ToOYBKm1Pyc00HG6+4fAeSP8YxEc2f77/RofAXUt3w2aXn9g+f/ytSG8f61K/Sq9T3ajRIMDASqf0HFgB+v21yxsFRRI1AnAG8ZFN6fcfaGuTdD0G4PYeorUVk4fgoSGdA1oHAyud0ntgBegviOHkrhRJ9Hb+UWT4yHoJszZa/X598+sxAL9yt4Jp/f8bgbt6B5a7y0mYyW8tE2bT28UCArhSXYfC0vKQ/vLV+5xukYKtH6ERjlMf8dgxhkBHija/Hu/NHe1yD7lsr8Ovd2pcMNTATT4MrMKENLInr7gML206rtmvZ73P6RYJ2IISGuH4I4LHjnFcra5HlAkIpCh/y+uxdE1udAi8u++cprlXV6oDK4qqJdaxCiNazDnWEquWa0sPx0CkCLepj3jsGEdecRleeP9YQEFVcy2vx0qn3wkGIxdgZmAVJvQycbDWBQ8jmV6OgUih5o+IYE2iLhePHeOQUy3dZAKeurMb5o+/VdY63V2Pxw3MwMrHbkdKQoyfe+o/oxdgZldgmNBLF5weCh5GokaHwNp953RxDEQKtX5E6KH7TS/XD3Kved7blWt1PpPMhQDGDMjA8B6p+P1ez1163q7H0sTMFdUN6rwJmUwwfgFmBlZhQi9dcKxaHnrubszesBtWHWr8iNBL8rterh/UmtLzW3L5Wq3f12OtJmZOiY/BkkcGGT6fj12BYUJPXXB6n9MtnHjKi/GG3bDqCHTuRD11v+np+kHf8ef8lkjfldLrsVYTM4/onoyDv8wOi/sDW6zChN664PQ+p5s3RhlurvQCyG5Y9QUyd6Keut/0dv0IBbXP85brG9YtBUcvXPV7/f4GOO6+KyXXY60mZj54vhLDf70LS39k/BYrBlZhQo9dcEac3FUP+S5yKbkAshs2ePz9EaGn7jc9Xj+CSe3z3N36WpZCULr+QAIcd9+V3Ouxlt29lTca8Ny6Y1ht8J4NdgWGET13wWk96kkOow03V3IB1MMxEM6km9ZDQzojq1earABEb91ver5+qEnt89zT+lpe4pSu398AZ3b2LQF9V3ro7n3lTyd0eY+Qiy1WYUaPXXBGaAUyYrFHuRfA+eNvxU/u6qGb/aYmeux+0+P1Q01qn+dKuuuUrt/fAKc8wBk3pONSy+ltKm/cxIEvywOe0kYrbLEKQ/78eg4Wo7QCGbHYo3QB9PTtmtAUwDKo0qdAk9+DuV96uX6ozd/z3FOLu9LuOiXXkavV9bLX29wfCy8gZ80BjFpW4Nf1NTrKhAcHa/+Dt7C0XOtd8BtbrChojNQKFEi+i1bJ7pGWFxOOAkl+J+X8Oc+9tbjX3XQEZT8aHQKLd5T4tW6JvyU7Gh0C247r4QcvuwIpRIwyYg3Q16gnX/zNd9G6m5M3ZuML9+43PVF6nvuqMzY7u09Q9kONkXn+/njValRgS1k9jdkNCDCwMhStb+JK6WnUky/+5Lvopbgjb8zGZ8QRtEak5DyX0+K+4dBFpCaYUSGz205u3pxa10R/frzq4XqcHB+DkQY+H5hjZRBGyVVqTm+jnryNTFSa76Kn4o5AeOfFEKlFyXkup8XdZq/DCIWDC+R0z6t9Tbx8rVb2yGw9jAr8zcSBhr6GKQ6s9uzZgwkTJiAzMxMmkwlbt251eX7z5s0YM2YM0tLSYDKZYLVaW63DZrNh6tSpsFgsSEhIwNChQ/GnP/3JZZnu3bvDZDK5/Fu6dKnLMidOnMDdd9+NuLg4dOnSBcuXL2+1rU2bNqFfv36Ii4vDoEGDsHPnTqVvWXN6u4nLJTe5OhSjnvKKyzBqWQFy1hzArI1Wt8mdSoabGzHZncgbI5REUYPc81xuy02vDgmylktLMMtuxZaunWo5f6XG5/Wv5ba1DGtSEmI13HrgFHcFVldXY/DgwXj66afxyCOPuH1+1KhRmDRpEqZPn+52HU888QQqKyuxbds2pKen4/3338ekSZNw5MgR3H777c7l/v3f/91lHe3bt3f+3263Y8yYMcjOzsbq1avx2Wef4emnn0ZycjKeeeYZAMD+/fuRk5ODJUuW4Ic//CHef/99TJw4EceOHcPAgQOVvnXNGClXqTm9JFcr6bKT261mpG5OIl+MlmbQnD95p3LOc7ktN1k90/GnY5e8XqNTE2JQOPcHMLeR15YhXTufW3dM1vKemAAkxcfgzV1nZKcseLtuS2LbRPmduC/HrhKbru5lSikOrO6//37cf//9Hp+fOnUqAOD8+fMel9m/fz9WrVqF4cOHAwBeffVVvPHGGzh69KhLYNW+fXtYLBa361i/fj3q6+vx7rvvwmw2Y8CAAbBarfjtb3/rDKxWrFiBcePG4eWXXwYALF68GPn5+Xj77bexevVqRe9bS0a+iWudXO3PyEQ5+S566+Yk8pdecgX9EUhA6Os8l5uPNbJXmjMQAdz/gPzNw4NkB1WScQMzMCf7Fryx64yi1zXftrQvSkdme7puJ8fHoLKmIahBFQBssV7CL8cbd0SzJjlWd955Jz744ANUVFTA4XBg48aNqK2txb/+67+6LLd06VKkpaXh9ttvx2uvvYabN286nyssLMQ999wDs9nsfGzs2LE4ffo0rl696lwmOzvbZZ1jx45FYWGhx32rq6uD3W53+ac1o9/Exw3MwN7c0dgwfSRWTB6CDdNHYm/u6JBcrIPVZaenbk4ifxk1zQDwnXe6YtfZgLo1leRjBatq/czRvWFJ9K9bzJIUhznZfVBZ0+BxGW/Xv5bX7fXTRiCuTbRf+6JURXWDodMoNBkV+OGHH+LRRx9FWloa2rRpg/j4eGzZsgW9e/d2LvOzn/0MQ4cORWpqKvbv34+5c+eirKwMv/3tbwE05Wn16NHDZb2dOnVyPpeSkgKbzeZ8rPkyNpvN474tWbIEixYtUuutqkKPFZqV0mrUU7Ba+/TSzUkUCKOmGcgJCJu39PjbramkxT0Yo3Ojo0z41Q/74/n3i2QtP/PeXujTqb1z29tPfCPrdbaqGx63L33vhaXlsNlD1yuixx4YuTQJrObPn4/Kykrs2rUL6enp2Lp1KyZNmoRPP/0UgwYNAgC8+OKLzuVvu+02mM1mPPvss1iyZAliY4OX2DZ37lyXbdvtdnTp0iVo25ODN3H/BbO1T+tuTqJAyb15hfKGKofSWkuBdGsqCZjU/gGZV1yGxTs+l738Xb07uGw/vZ28e+XiHZ+jrTna+dm4y1sLdaCj1x4YOUIeWJWWluLtt99GcXExBgwYAAAYPHgwPv30U6xcudJj7tOIESNw8+ZNnD9/Hn379oXFYsG3337rsoz0t5SX5WkZT3lbABAbGxvUwM1fvIn7J9itfawhRUYm9+a1ePtJtI2J0s11RulNPtCZHrRocfeU++aOx+uYzB7Qiup6Z+AJwG3e2rBuKfJWpgKjp1GEPLCqqakBAERFuaZ3RUdHw+HwnBBntVoRFRWFjh07AgCysrIwb948NDQ0ICYmBgCQn5+Pvn37IiUlxbnM7t27MXv2bOd68vPzkZWVpeZbChnexJULRWsfizuSUfn64SGpqG7QVSK7P60Zeu3WdEfJ5M7ermNXqusUbXfu5s9w1U1OVllVLbafCF2tRKP3wChOXr9+/TqsVquzPtW5c+dgtVpx8eJFAEBFRQWsVitKSprmOTp9+jSsVqszr6lfv37o3bs3nn32WRw6dAilpaV4/fXXkZ+fj4kTJwJoSjp/8803cfz4cXz55ZdYv3495syZg8cff9wZND322GMwm82YNm0aTp48iQ8++AArVqxw6cabNWsW8vLy8Prrr+PUqVNYuHAhjhw5gpkzZ/r9gWmNhSCVC1ZiKZHReUvQdkcvieyB1FoyQu6Okq5Ob9cxJQGoANwGVaScSQih6Cz529/+hnvvvbfV408++STWrl2LtWvX4qmnnmr1/IIFC7Bw4UIAwNmzZ/HKK69g7969uH79Onr37o2XXnrJWarh2LFjeP7553Hq1CnU1dWhR48emDp1Kl588UWXbroTJ07ghRdewOHDh5Geno6f/vSnyM3Nddnupk2b8Oqrr+L8+fPo06cPli9fjgceeED2+7Xb7UhKSkJVVRUSExNlv470x0jzLBKFUl5xGX65pVjW1Cwbpo/URYuP1FUGKJuut/n+6/Wa8JH1EmZttPpcbua9vTHnvls87nOjQ2DUsgKfLZJ6InVr7s0dHfB3odX9W3FgFWkYWBFRJNhy7GvM+fC4z+VWTB6Ch4Z0DsEe+bbzRBle/UheQNjyhq2noqgtAzyHEJjy+4M+XycnyFWSq6UnagTwWt2/OQkzERHBktRW1nJ6Ga3VNGKuxCWoSoiNRnVdo898Sj0VRXUX4FkSY5EcH4OqmoaAB91IqRALt52Eza4s50pLRuiy9YSTMBMRkaGK3noqDlpT1wigaRqX5prnIempKKqn9/GtvQ6V/wyq5EwK707zuR+T2pqx5xejMSf7FrfLmv75L7nF56YlvQTw/mCLFRERGaZenpxpqtrGRGPltKG4Ul3XKndKL0VR5byPpPgYxLWJdqkjJqfEjrtWsNQEMyYOycSc7Fuw4dBFt+t0OITsYqTBlJoQo4sA3l8MrIiICIAx6uXJDYyiokxuc8H0MveqnPdRWdOA9dOGIirKJDvB3lM3Z0V1Pd7ddx5AU1fjnOw+6J6e4LLOwtLywN+YCh4e0lnzAD4QDKyIiMhJ7/XyAg2M9DL3qtz3caW6TvZgAbn1r2z2Ory56yxWPT7UpVVuV4nn6d5CKbu/5yLeRsDAioiIXOi56G2ggZFe5l4NRoCndKqf5pXoGx0CW6yXZL82WPSSxxcIJq8TEZFhBJpk760oaihzyYIxWEDJnI7Nc8mApqCsolr7AqF6yOMLFAMrIiIfmo+wKiwt10X18UilRmCkh9kY5LyP+eP749C5CtnHXcV15eUUpC5JPZQ3mHZXd13k8QWKXYFERF7oqZAkNVEjyV4PuWTe3seDgzOweIey4y41wax4H6SuRj2UNzB6bpWEgRURkQd6KiRJrtQIjPSQS+bufVytrsML7xcpPu7kFnkFWueSXVU4YbPaokzAsG4pmu6DWhhYERG5IafOUPPkXwo9PQRGamj+PqT5/bwVMF247aTb407K2/KVwN6yy7TRIbB4x+eBvYkAOQRw9MLVsPg+mWNFROSGkkKSRJJA8/HkjOyz2evwdsEXrR6X8rZ8hfktc8mUjiYEXPPC1PpZoYc8LzWwxYqIyA29FJIk41AjH0/u8fTGrjPoa2nXar2e8rYkqQkxmD/+VpfX+XMMS/lsADxuSyk95HmpgS1WRERu6KWQJBmDp3n/pLyovOIyWes5f6VG9jY9zWk4bmAG5o/v7/Y1V6sb8ML7RS77c/5Ktextzry3F9ZPG4H//PFg1N10IKmtGX9/+V5MHdlV9jpa0tM8lGpgixURkRt6KSRJ+qdWPl6jQ2DDoYuyt+tpTsOmnKkSt69puT/5JTa8seus7G3W1Dfipf873qpV7pZO7WWvw51wqF8lYYsVEZEbeikkSfqnVj7eoXMViop8Au678eTuz4Evy7HoY/cBmCfv7jvvtlXu72f+oWg9zf1oaOewGl3LwIqIyAM9FJIk/fM3H69lorut6obibbvripa7P/u/uKJKblSg5XLjY8Or8yy83g0Rkcr0UEiS9M2ffDx3ie5KCnx664qWuz9r95+Xvb1g6pYar/UuqIqBFRGRD+FSL4mCQ2k+nqfCs1er62Vtz1dXtK/9kVTXN8raXrB1ah+r9S6oil2BREREAVCSj+cr0V0OX13R3vZHj37951NhNf8mAysiIqIAyc3Hk1uMMzUhptXf0+7qjg3TR2Jv7mif+X3S/qT4MX9gqIVboV12BRIREalATj6e3MTy+T8cAEtiXEB5feMGZuBGfSPmfHjc57LJbWNQeaNB0frVlF9iC5vudgZWREREKvGVjyc3sdySGKdKoCF3Yuaf3NkdI3qmwVZ1A/u+uIL/O3Yp4G0r8ZH1G8wbHx7lS9gVSEREFCJSYrmn8EHtKuTDe6TCkug7OfyDI19heI9UPDz0e/jPSUOw+vGhyEgK3awC5dX1YdMdyMCKiIgoREJdeDY6yoSc4b6nm2mZ5zRuYAb+/vK9mD/+VvxLn3RV9sWXcJl3k12BREREIdLoEEhqa8bTd3XHFuslVFR/l9dkUThhs1zd0xNkLdc8sHFXZyvYwmXeTQZWREREIeCpKOjEIZm4r78laIVnlRYw9VRnK5gsibFhM+8mAysNNDoEqzgTEUUQb0VB39t3Pqj3ASUFTL3V2QqmnOFdw+Y+yMAqxNz9YskIUvMvERFpz1dRUBOARR+X4L7+lqAEF1Je14x1x2CC+0KkUl5XYWl5SLv/JHK7K42AyeshJP1icTcz+Ix1x5BXXKbRnhERUbD4KgoqEPwimVLB0KT4mFbPNX9MqwTy9HbhM60NA6sQkTONwaKPS8KqrD8REckPVkIR1FTWtC4CWlXT4Pxxr1UC+eEwKbUAMLAKGT38YiH9a3QIFJaW4yPrJRSWljPQJgoDSpPHg0H6ce9O8x/3w7qltJpOJxTe3H02bHptmGMVInr6xUL6xPw7ovCkJHk8WOT+uD964Sr+46GBeP79oqDtiyfBzDMLJbZYhYgefrGQfjH/jih8hbooqDtKftw/cFsmnr2nR9D2xZNw6bVhYBUioZ7GIJIZrTuN+XdErox2DsshJY9bWkwTY0mKw6rHhwbcKu3rM1P6437uA/3xu8eGIjXBHNB+KRUOvTbsCgwRb8NdQ/WLJRIYsTtNSf5dIJOysn4aGUGwz2Etz4NxAzNwX3+L6tuX85n50x35wG0ZGDuwaX9t9lrsO/uPoE/OHA69NgysQkj6xdLyBAjWNAaRxlMBPqk7TY1fhcEQivw7IwacFHmCfQ7r4TyIjjIF9AOpJbmfmb8/7pvvb9uYqKAGVuHSa8OuwBAbNzADe3NHY8P0kVgxeQg2TB+JvbmjeXMLkJG704Kdf8f8LTKCYJ/D4XgeKP3MAumO9DaqUC2Tvx8e1dfZYqUBtX+xUOi604IhmCOGtK74TCRXMM/hcD0P/PnM/O2O9LUtNXRPjw/q+kOFLVYUFoxcziKYI4ZYP42MIpjncLieB/5+ZtKP+4eGdEZWrzRZ15ZQXDvDIb8KYGBFYcLo5SyCNWLIyAEnRRa5U5r4cw6H63kQyutesKecCZf8KoBdgRQm9FCAL1DBGDFk9ICTIkNecRkWbjvpdZlAzuFwPQ9Cet0LcnpqOI2KZ4sVhQU9FOBTgz9N9N6wfhrpnZRUbrPXeVwm0HM4XM+DYF333NXEulLt+fsJ1L8N7RxWA7gYWFHYCHYBPiMKl4CTwpO3pPLmAj2HtTgPQlXkVO3rXl5xGUYtK0DOmgOYtdGKnDUHMGpZAc5fqVZzt1385pHbgrZuLZiEEPobf64jdrsdSUlJqKqqQmJiota7wyKPMvAzak0P9XuIWiosLUfOmgM+l1s/bQTu6pPu8Xm553yozgMtzjc1rnueamJJda+S42NQVdOgaq9ggjkaJxaODco1Wqv7N3OsDIQ3R3lYzqK1YFV8JgqE3GRxb91QSq6LoTgPtCpUHOh1T05NrIZGh+qpVtX1jbosgxMIBlYGoXVVcbYCGR8DTtKbQJPK/bkuBvM8MHK9LDl1qqrrGoOybaONxvSFgZUBaH2ysqWMiIIhkFFtWl8X3dFboWI5P4ilZf6sYeX581dqNNt2MDCwMgAtT1atW8qIKHwFMjm93oIYQF/1suT8IHa3jBbe3HUGfS3twuZewlGBBqDVyWrk+feIyBj8HdWmpyBGoqRrM5ijBuXMi+hpGa2E072ELVYGoFVxOz3+IiSi8ONPUrkei37K7dq8Wl2HUcsKgpJeIbeLVAgR7JqfsoXbvYQtVgagVXE7Pf4iJKLwpLQ4rh6Lfsqpl/Xg4Ay88H6R19akQMj9QeytIKtWwuVewsDKALQq8qjHX4RERIB+i99669pc+dhQbDteFtT0CiMHJ+FyL2FXoEFIJ2vLRENLEEfnhcP8e0QUvrS4LsrdL3ddm6FIrzBicBJu9xIGVgYS6iKPgYzYISIKBb0Wv3VXLysU6RVyfxALIfCtvU43eVbhdC9hYGUwoS7yqNdfhEREEqMUvw1FeoXcH8QAPC4TymArLcGMXz88MKzuJZwr0Ae9zRWoFVZeJyIKTKNDYNSyAp+tSXtzRwd8ffW3jlVGUhzmj++PxTtKvO5nUnwMKmsaAtrHlPg2OPjL+2BuE5x0b63u3wysfGBgRUREapHqRwHuW5PULLqspPJ6y2Xk7CcAvPjhcdTU+zfVzeogF5hmYKVTDKyIiEhNRpkmTM5+NjoE3tp9Br//9ByqmwVYGUlxeHBwBj448nWrlq3k+BgsfWRQ0N8rAyudYmBFRERqM0p6hdz99LRco0PgwJflKCwtByCQ1TMdI2XUKVMDAyudYmBFRERkPFrdv1kglIiIiEglDKyIiIiIVMLAioiIiEglDKyIiIiIVMLAioiIiEglDKyIiIiIVKI4sNqzZw8mTJiAzMxMmEwmbN261eX5zZs3Y8yYMUhLS4PJZILVam21DpvNhqlTp8JisSAhIQFDhw7Fn/70J5dlKioqMGXKFCQmJiI5ORnTpk3D9evXXZY5ceIE7r77bsTFxaFLly5Yvnx5q21t2rQJ/fr1Q1xcHAYNGoSdO3cqfctEREREsigOrKqrqzF48GCsXLnS4/OjRo3CsmXLPK7jiSeewOnTp7Ft2zZ89tlneOSRRzBp0iQUFRU5l5kyZQpOnjyJ/Px8bN++HXv27MEzzzzjfN5ut2PMmDHo1q0bjh49itdeew0LFy7EO++841xm//79yMnJwbRp01BUVISJEydi4sSJKC4uVvq2iYiIiHwTAQAgtmzZ4va5c+fOCQCiqKio1XMJCQnij3/8o8tjqampYs2aNUIIIUpKSgQAcfjwYefzf/7zn4XJZBKXLl0SQgjxu9/9TqSkpIi6ujrnMrm5uaJv377OvydNmiTGjx/vsp0RI0aIZ599VvZ7rKqqEgBEVVWV7NcQERGRtrS6f7fRIpi788478cEHH2D8+PFITk7Ghx9+iNraWvzrv/4rAKCwsBDJycm44447nK/Jzs5GVFQUDh48iIcffhiFhYW45557YDabncuMHTsWy5Ytw9WrV5GSkoLCwkK8+OKLLtseO3Zsq+7L5urq6lBXV+f8u6qqCkBTCxkREREZg3TfFiGeYEaTwOrDDz/Eo48+irS0NLRp0wbx8fHYsmULevfuDaApB6tjx46uO9qmDVJTU2Gz2ZzL9OjRw2WZTp06OZ9LSUmBzWZzPtZ8GWkd7ixZsgSLFi1q9XiXLl2Uv1EiIiLS1LVr15CUlBSy7WkSWM2fPx+VlZXYtWsX0tPTsXXrVkyaNAmffvopBg0apMUuOc2dO9ellcvhcKCiosKZjB8ou92OLl264KuvvuLcgwrwc/MPPzf/8HPzDz83//Gz84+3z00IgWvXriEzMzOk+xTywKq0tBRvv/02iouLMWDAAADA4MGD8emnn2LlypVYvXo1LBYLLl++7PK6mzdvoqKiAhaLBQBgsVjw7bffuiwj/e1rGel5d2JjYxEbG+vyWHJysvI36kNiYiJPHj/wc/MPPzf/8HPzDz83//Gz84+nzy2ULVWSkNexqqmpadpwlOumo6Oj4XA4AABZWVmorKzE0aNHnc8XFBTA4XBgxIgRzmX27NmDhoYG5zL5+fno27cvUlJSnMvs3r3bZTv5+fnIyspS/40RERFRxFMcWF2/fh1Wq9VZn+rcuXOwWq24ePEigKb6U1arFSUlJQCA06dPw2q1OvOa+vXrh969e+PZZ5/FoUOHUFpaitdffx35+fmYOHEiAODWW2/FuHHjMH36dBw6dAj79u3DzJkzMXnyZGeT3mOPPQaz2Yxp06bh5MmT+OCDD7BixQqXbrxZs2YhLy8Pr7/+Ok6dOoWFCxfiyJEjmDlzpt8fGBEREZFHSocRfvLJJwJAq39PPvmkEEKI9957z+3zCxYscK7jzJkz4pFHHhEdO3YU8fHx4rbbbmtVfqG8vFzk5OSIdu3aicTERPHUU0+Ja9euuSxz/PhxMWrUKBEbGys6d+4sli5d2mp/P/zwQ3HLLbcIs9ksBgwYIHbs2KH0LauqtrZWLFiwQNTW1mq6H0bDz80//Nz8w8/NP/zc/MfPzj96/NxMQoR4HCIRERFRmOJcgUREREQqYWBFREREpBIGVkREREQqYWBFREREpBIGVjJcunQJjz/+ONLS0tC2bVsMGjQIR44ccT4vhMCvfvUrZGRkoG3btsjOzsbZs2dd1lFRUYEpU6YgMTERycnJmDZtGq5fv+6yzIkTJ3D33XcjLi4OXbp0wfLly1vty6ZNm9CvXz/ExcVh0KBB2LlzZ3DedIC6d+8Ok8nU6t8LL7wAAKitrcULL7yAtLQ0tGvXDj/60Y9aFXO9ePEixo8fj/j4eHTs2BEvv/wybt686bLM3/72NwwdOhSxsbHo3bs31q5d22pfVq5cie7duyMuLg4jRozAoUOHgva+A9XY2Ij58+ejR48eaNu2LXr16oXFixe7zHXF4829a9euYfbs2ejWrRvatm2LO++8E4cPH3Y+z8+tyZ49ezBhwgRkZmbCZDK1mjtVT5+TnH0JFV+f2+bNmzFmzBjnLB1SSaLmIvG65+1za2hoQG5uLgYNGoSEhARkZmbiiSeewDfffOOyDsMdb5qNRzSIiooK0a1bN/GTn/xEHDx4UHz55ZfiL3/5i/jiiy+cyyxdulQkJSWJrVu3iuPHj4sHH3xQ9OjRQ9y4ccO5zLhx48TgwYPFgQMHxKeffip69+4tcnJynM9XVVWJTp06iSlTpoji4mKxYcMG0bZtW/Hf//3fzmX27dsnoqOjxfLly0VJSYl49dVXRUxMjPjss89C82EocPnyZVFWVub8l5+fLwCITz75RAghxHPPPSe6dOkidu/eLY4cOSJGjhwp7rzzTufrb968KQYOHCiys7NFUVGR2Llzp0hPTxdz5851LvPll1+K+Ph48eKLL4qSkhLxX//1XyI6Olrk5eU5l9m4caMwm83i3XffFSdPnhTTp08XycnJ4ttvvw3ZZ6HEr3/9a5GWlia2b98uzp07JzZt2iTatWsnVqxY4VyGx5t7kyZNEv379xd///vfxdmzZ8WCBQtEYmKi+Prrr4UQ/NwkO3fuFPPmzRObN28WAMSWLVtcntfT5yRnX0LF1+f2xz/+USxatEisWbNGABBFRUWt1hGJ1z1vn1tlZaXIzs4WH3zwgTh16pQoLCwUw4cPF8OGDXNZh9GONwZWPuTm5opRo0Z5fN7hcAiLxSJee+0152OVlZUiNjZWbNiwQQghRElJiQAgDh8+7Fzmz3/+szCZTOLSpUtCCCF+97vfiZSUFFFXV+ey7b59+zr/njRpkhg/frzL9keMGCGeffbZwN5kCMyaNUv06tVLOBwOUVlZKWJiYsSmTZucz3/++ecCgCgsLBRCNJ2MUVFRwmazOZdZtWqVSExMdH5Gv/jFL8SAAQNctvPoo4+KsWPHOv8ePny4eOGFF5x/NzY2iszMTLFkyZKgvM9AjR8/Xjz99NMujz3yyCNiypQpQggeb57U1NSI6OhosX37dpfHhw4dKubNm8fPzYOWNzo9fU5y9kUr7gIryblz59wGVrzuef/cJIcOHRIAxIULF4QQxjze2BXow7Zt23DHHXfgxz/+MTp27Ijbb78da9ascT5/7tw52Gw2ZGdnOx9LSkrCiBEjUFhYCAAoLCxEcnIy7rjjDucy2dnZiIqKwsGDB53L3HPPPTCbzc5lxo4di9OnT+Pq1avOZZpvR1pG2o5e1dfXY926dXj66adhMplw9OhRNDQ0uLyXfv36oWvXri6f2aBBg9CpUyfnMmPHjoXdbsfJkyedy3j7POrr63H06FGXZaKiopCdna3bz+zOO+/E7t27cebMGQDA8ePHsXfvXtx///0AeLx5cvPmTTQ2NiIuLs7l8bZt22Lv3r383GTS0+ckZ1+MhNc9eaqqqmAymZxz9BrxeGNg5cOXX36JVatWoU+fPvjLX/6CGTNm4Gc/+xn+8Ic/AIBzqp7mJ4L0t/SczWZDx44dXZ5v06YNUlNTXZZxt47m2/C0jPS8Xm3duhWVlZX4yU9+AqDpfZjN5laTW7f8zPz9POx2O27cuIErV66gsbHRUJ/ZK6+8gsmTJ6Nfv36IiYnB7bffjtmzZ2PKlCkAeLx50r59e2RlZWHx4sX45ptv0NjYiHXr1qGwsBBlZWX83GTS0+ckZ1+MhNc932pra5Gbm4ucnBznhMpGPN7aKFo6AjkcDtxxxx34zW9+AwC4/fbbUVxcjNWrV+PJJ5/UeO+M4X/+539w//33O+d5JM8+/PBDrF+/Hu+//z4GDBgAq9WK2bNnIzMzk8ebD//7v/+Lp59+Gp07d0Z0dDSGDh2KnJwcl8nciUifGhoaMGnSJAghsGrVKq13JyBssfIhIyMD/fv3d3ns1ltvdU46bbFYAKDVyI5vv/3W+ZzFYsHly5ddnr958yYqKipclnG3jubb8LSM9LweXbhwAbt27cL/+3//z/mYxWJBfX09KisrXZZt+Zn5+3kkJiaibdu2SE9PR3R0tKE+s5dfftnZajVo0CBMnToVc+bMwZIlSwDwePOmV69e+Pvf/47r16/jq6++wqFDh9DQ0ICePXvyc5NJT5+TnH0xEl73PJOCqgsXLiA/P9/ZWgUY83hjYOXDXXfdhdOnT7s8dubMGXTr1g0A0KNHD1gsFuzevdv5vN1ux8GDB5GVlQUAyMrKQmVlpcsv54KCAjgcDowYMcK5zJ49e9DQ0OBcJj8/H3379kVKSopzmebbkZaRtqNH7733Hjp27Ijx48c7Hxs2bBhiYmJc3svp06dx8eJFl8/ss88+czmhpBNOCnR9fR5msxnDhg1zWcbhcGD37t26/cxqamoQFeV6WkZHR8PhcADg8SZHQkICMjIycPXqVfzlL3/BQw89xM9NJj19TnL2xUh43XNPCqrOnj2LXbt2IS0tzeV5Qx5vilLdI9ChQ4dEmzZtxK9//Wtx9uxZsX79ehEfHy/WrVvnXGbp0qUiOTlZfPTRR+LEiRPioYcecjs8+fbbbxcHDx4Ue/fuFX369HEZLlpZWSk6deokpk6dKoqLi8XGjRtFfHx8q+Gibdq0Ef/5n/8pPv/8c7FgwQJdDeNuqbGxUXTt2lXk5ua2eu65554TXbt2FQUFBeLIkSMiKytLZGVlOZ+Xhh2PGTNGWK1WkZeXJzp06OB22PHLL78sPv/8c7Fy5Uq3w45jY2PF2rVrRUlJiXjmmWdEcnKyy6gbPXnyySdF586dneUWNm/eLNLT08UvfvEL5zI83tzLy8sTf/7zn8WXX34p/vrXv4rBgweLESNGiPr6eiEEPzfJtWvXRFFRkSgqKhIAxG9/+1tRVFTkHIWlp89Jzr6Eiq/Prby8XBQVFYkdO3YIAGLjxo2iqKhIlJWVOdcRidc9b59bfX29ePDBB8X3vvc9YbVaXUr0NB/hZ7TjjYGVDB9//LEYOHCgiI2NFf369RPvvPOOy/MOh0PMnz9fdOrUScTGxoof/OAH4vTp0y7LlJeXi5ycHNGuXTuRmJgonnrqKXHt2jWXZY4fPy5GjRolYmNjRefOncXSpUtb7cuHH34obrnlFmE2m8WAAQPEjh071H/DKvnLX/4iALT6LIQQ4saNG+L5558XKSkpIj4+Xjz88MMuFyAhhDh//ry4//77Rdu2bUV6err4+c9/LhoaGlyW+eSTT8SQIUOE2WwWPXv2FO+9916rbf3Xf/2X6Nq1qzCbzWL48OHiwIEDqr5PNdntdjFr1izRtWtXERcXJ3r27CnmzZvncpHh8ebeBx98IHr27CnMZrOwWCzihRdeEJWVlc7n+bk1+eSTTwSAVv+efPJJIYS+Pic5+xIqvj639957z+3zCxYscK4jEq973j43qTSFu39SzUMhjHe8mYRoVtKZiIiIiPzGHCsiIiIilTCwIiIiIlIJAysiIiIilTCwIiIiIlIJAysiIiIilTCwIiIiIlIJAysiIiIilTCwIiIiIlIJAysiIiIilTCwIiIiIlIJAysiIiIilTCwIiIiIlLJ/weCOh2K5xxSXAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "bc01409d-d9e4-43a6-9fae-c76aa7bdc855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous unit avg and sd usage are 1.00552 0.0995\n",
      "amped unit avg and sd usage are 1.00884 0.09867\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prevUnitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "ee512b2b-4daa-4a6b-9452-e8dfdfdebc88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the current barredJettisonSet (usually CCB's) is set()\n"
     ]
    }
   ],
   "source": [
    "#CHECK ON BARRED JETTISON SET BEFORE RUNNING BELOW\n",
    "print(\"the current barredJettisonSet (usually CCB's) is\",barredJettisonSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "dd7d9818-344c-4150-917b-57a4ee051c92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 3083 overPopped HDs to within 1080\n",
      "working on squaring down HD 0 time is now 0\n",
      "working on squaring down HD 543 time is now 1\n",
      "working on squaring down HD 1052 time is now 3\n",
      "working on squaring down HD 1516 time is now 4\n",
      "working on squaring down HD 2115 time is now 5\n",
      "working on squaring down HD 2804 time is now 7\n",
      "working on squaring down HD 3394 time is now 8\n",
      "working on squaring down HD 4068 time is now 9\n",
      "working on squaring down HD 4675 time is now 10\n",
      "working on squaring down HD 5351 time is now 12\n",
      "working on squaring down HD 5953 time is now 13\n",
      "working on squaring down HD 6559 time is now 14\n",
      "working on squaring down HD 7027 time is now 16\n",
      "working on squaring down HD 7436 time is now 17\n",
      "working on squaring down HD 7968 time is now 19\n",
      "working on squaring down HD 8684 time is now 20\n",
      "We have addressed overpop in a total of 3083 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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zRmNv7ljdIy9S3+Mb5owI+JRCIHpCBeKa1ICN/APcUUXyPiPk4I/92xcowqUDgfytnuiga5wZi6b2x7gMG0Yty5dVi+SvzTDBYpLdragXoRStCLQ6E187PwMZiioSoQwJLh1Itpj1XkJQo2Q+ohixpgg0trR7XEV+LZI/N8NgStNRDZR/UKvzM5AJx+41Ijwg41M9oM8Nn7DGRql+TnexBVys79jAdkqcmXfpSpPfTBtZ49DTF69KOj5QiorFTFODXRhojVjnJxA67vdsVFHphAKCCEQowqUDVVe9u8kIacy7qyc+OvQ9AP9aabDpwk+Lz0s6PrmTGb/edFR100auiBkfgZimo2iFepBPFUEENyS4dCBQIhDBhgHA3/efFR3KrBYMOiwtEi0m1DS08KYx42OjUHqhTvXNUI51SKCl6UK1qNsTLV9nMKWUCYLwhgSXDgxLT0SixSRr6DFxXQDJpWPAtHKRNm1QKt4rqOCtHattbMWr27+RdC6pm6Fc65BAKiqWW8cWrOJM6+J18qkiiOBGdg3X7t27MWXKFKSmpsJgMGDz5s3Ox1pbW5Gbm4sBAwbAYrEgNTUVP/vZz3DhwgW3c1RXV2PWrFmIi4tDfHw8Zs+ejatX3etTjh07htGjRyM6Ohrdu3fH8uXLvdayadMm9O3bF9HR0RgwYAC2b9/u9jjDMHjxxReRkpKCmJgYZGdn4/RpbUbLCBFhNGDaoFS9l6ELFlOEptdbOLkf9i/IljXU2JNxGTbOWiQlSN0MxdJHLHPH3IwNc0Zgb+7YgBFbcurYdpRUYuTSLzFz3X7M21iMmev249ZX/oXtx9w/MwINKa/Tl6HdXMgZ5E0QROAhW3A1NDRg4MCBWL16tddjjY2NOHLkCBYuXIgjR47gk08+wcmTJzF16lS342bNmoXjx48jLy8PW7duxe7du/HII484H6+vr8f48ePRo0cPHD58GK+99hoWLVqEtWvXOo/Zt28fZs6cidmzZ6OoqAjTpk3DtGnTUFJS4jxm+fLl+NOf/oQ1a9bgwIEDsFgsmDBhApqa9A+5j8uw6b0EXWj4oTg9PiYKT93VC107y+/YTLSYJImnFGs0fj4yHaZII+90eiFcN7CczBTszR2LD38xHPEx8ov2hTZDro1ZaiSsV9fOmhcV8wkJuUXdO0oq8dgHR2Cvd69prGlswxPri/DL9YcDsgBcyut87pOvMXJpvpuQHLUsX3HjBHC98xPwfh8HWkqZIAhvfDI+NRgM+PTTTzFt2jTeYw4dOoRhw4bh7NmzuPHGG3HixAlkZGTg0KFDuPXWWwEAO3bswKRJk/D9998jNTUVb731Fp5//nnY7XaYTCYAwHPPPYfNmzfjm286Ujf33XcfGhoasHXrVue1RowYgUGDBmHNmjVgGAapqal45pln8Otf/xoAUFdXh65du+L999/HjBkzJL1GfxmntTsY/PjVPM3qkQINdkuYfEsKth6TvgnFx0bh99MG4In1/K7r7Pk9O+G4UkDs4HA+6wLPcygxrRUybeRLS834cXdJg76FjEP9kaoTSqNZY0ySzU6HpSdi6Ct5qBWZIxofG4Wl9w4IiOgdi1LjYrXMO0PZh4sg1CTsjE/r6upgMBgQHx8PACgsLER8fLxTbAFAdnY2jEYjDhw4gHvuuQeFhYW4/fbbnWILACZMmIBly5ahpqYGCQkJKCwsxNNPP+12rQkTJjhTnOXl5bDb7cjOznY+brVaMXz4cBQWFvIKrubmZjQ3X//GXV9f7+st4CTCaMArd2fiifVFfjl/oMN27u09UyXrebWNrTj6fQ3iY6N4N2u+zYevYy6v1C7ZaFFJQTLfuYQ8lVbsPI342CjUNbYqMg71x6Ys5gH1vyPTJJ3n0pUm7C+7LCq2gI6/d6D5SyktSvelW9UV6vwkiODEr4KrqakJubm5mDlzplNd2u12dOnSxX0RkZFITEyE3W53HpOenu52TNeuXZ2PJSQkwG63O3/neozrOVyfx3UMF0uWLMHLL78s96XKpt3BIMFixpg+N2DXyf/6/XqBCANI2nQ9eXt3Oe9j87N7Y+7YnrybD+vvw0Z/th67gC6do/GfZ8dImnsotQbrf4Z0w+jeN/CeSywt5Xq0VONQ9jXlldrxbkGF13l9MceUsl6pFhpdOkej4Iz09zwD30WKmvhSlK6WdUOgud8TBCGO3wRXa2srpk+fDoZh8NZbb/nrMqqzYMECt8hZfX09unfvruo15HgrhQPRUUY0tTpUOdf7+8oxd2xPwWOEoj93D+om+FyxkTUs/zhyHtkZXXk3RSmeSrWNrZif3RsbD50Tjb5JeU/5EmGRsl4xCw3XqFyBzMhmIPlLSX0PCEHWDfoRrF2xRPDjF8HFiq2zZ88iPz/fLXdqs9lw6dIlt+Pb2tpQXV0Nm83mPObixYtux7A/ix3j+jj7u5SUFLdjBg0axLt2s9kMs9l/o3fkeCuFC2qJLQCoaWzFqvzTmJfdG4D3h2tNQwueXC88GkUoXeM6skYIMWEjdcNNS47F3tyxghuEnPeU1AiL532z10tbL5+FhmdULuvmJKzadUbSOVkCRaQIjS2SClk36APVvxF6orrgYsXW6dOnsWvXLiQluX+oZ2Vloba2FocPH8bQoUMBAPn5+XA4HBg+fLjzmOeffx6tra2IiuroCMvLy0OfPn2QkJDgPObLL7/EU0895Tx3Xl4esrKyAADp6emw2Wz48ssvnQKrvr4eBw4cwOOPP672y5aEXG8lQhnvFVRg7thenLVZRgP3BslGf5775Gss+qzUTWB4fiDnZKbgqezeWLHzFO8axISNHE8lofSR0veUkHjh2pQSLdI6M8dl2DAsPVG0Jm7ETUmCdXhcJHcKjBmk7Q4G1hgTHh6Zhs3FF9z89GxxZjS1ORTX3oUagRRNCoc5lERgI1twXb16FWfOXP9mWl5ejuLiYiQmJiIlJQX/8z//gyNHjmDr1q1ob2931kslJibCZDKhX79+yMnJwZw5c7BmzRq0trZi7ty5mDFjBlJTO7yp7r//frz88suYPXs2cnNzUVJSgpUrV2LFihXO686bNw933HEHXn/9dUyePBkbN27EV1995bSOMBgMeOqpp/DKK6+gV69eSE9Px8KFC5GamirYVelPpHorEb5Re60jyvXGztNeH65CLgPXa8rcRQDXB3JacqyktXAJm3YHA4eDQXxMFGqvcQsOAzrsL+x111BYdpl3o1L6nuITfHybklg3rauQiDAaRIu6I4wGLL13AB4TiRS68vRHxbh/eA+kJcfqtnnzidF7BnVD9g9iM6/UTkO7EVjRJCk1iIFUJ0iEJrJtIf79739jzJgxXr9/6KGHsGjRIq9id5Zdu3bhzjvvBNBhfDp37lx8/vnnMBqN+OlPf4o//elP6NSpk/P4Y8eO4cknn8ShQ4eQnJyMX/7yl8jNzXU756ZNm/DCCy+goqICvXr1wvLlyzFp0iTn4wzD4KWXXsLatWtRW1uLUaNG4c0330Tv3r0lv14120q3FJ/HvI3FPp2DkIaQmFECKyj25o5FhNEg2RrA07ZBaf0e30b1adF5zP+oWPJ5PF+HK+0OBqOW5YuuTaqFhhR2lFTiuU++VtQ8ofXmzSdGuV6/v8VGIEWOuJBzr7RA6f9fieAm0GwhfPLhCgfU/IMp9e8hAgf2A5kVJ3yF01zCZvuxSlH/MD74Nqp39nyLxdtOyDrXGp7NTur703Msla9Cot3BYP+3l/HB/rPYffq/aGhul/Q8LTdvMTHK9ff2lygKpMgRF0rulb+R+mV35YxBoo0zRPAQaIKLZilqSMcMRd/m+hHidDJH4mpzm1/OzaYIhQqnuVJH249dwNwNwp5r1phIGA0G1HBEe/jSHokWk9exSpFalL5wcj/YrDGyhQSfAIkwGjCyZzJG9kxGwekqzHrngKR1aJkKktKl6Vmz5w/rhmCoQ1Jyr/wNzaEkAgHZo30I5bBmp4R/+cUo7rS2Grh+IOdkpnDOWLRZo73SS0+sLxKsHwOAumttnGKLhd2o3i8od468sVljZK2fFShcI3OkbjY2awyybk7C3YO6SR4rtKOkEqOWiY+6qWpo5jkDN66btz+RKkb92Ukpd3SSXgTCvfKE5lASgQAJLo2ZdEsqHr3df4Ig3Hn09nT88q5ePg2r5oLvA5mdsbhhzgisnDHIa5A0u0mqyeJtJ5xihd1IpCIkUPy1KckZaK00wuDvzTsQIiRyIkd6Egj3yhOaQ0kEAiS4dGDBpAwMvTFe72WEFJ2jI7BqxiAsmJQh+OGqBLEPZDZ1xBXx8VdnKitW8krteGlKhuzXySVQ/LEpyY3KiIk+Pvy9eQdChCQQI0dcBMK94kJqRJog/AUJLh1oaXPgyLlavZcRNHjWKaVYozFndLrb7680tePVL75xRkv4PlyVXv/hkWmwxphkp2vkbH62ODNscdLEhqtYGZdhw1sPDJEV6eITKGpvSnKjMnLFslabdyBESAIxcsRFINwrPsQi0gThT6hoXgf+XlhB5qcSsZgisH/BXW5zDqW4xedkprgN+WXnC8p1Bo+OMuJyQwveLajAuwUVsrvB5Gx+i6b2BwDJDuauYoV9rfu/vYwnPzwi6O8lZryp5nBkJVEZVvSJ2WdovXnzrYtvOLnaiI0UCiRTVb3vlRA0h5LQCxJcOlBxuUHvJQQNvxidDlOk0fkBybacSzUwZD9cs25O4nRAF8Nz7JDcbjCpc/fiYzuc3KWKDVcuXWlydgBWXW3GwyPTsGLnaZ+MN9XalJRGZTxFX0VVAzYcPAd7/fWiej02bzXFqFzkdsb6EymWF3reK4IIRMiHSwR/+Hi88OnX+ODAOVXOFcqYI40o/V2O2we0rwaG7Q4Gv/64GJ8WX1C8Lrk+QmzROMAftfL0lGp3MHi/oFySxxbXgGtWwLkainpG57Qwz1TiVyZ0Ltq89ffh0vv6BCEV8uEi0DmabrsUYk0RXr/ztXA4wmhAtwR5VgqeyPURGpdhw1PZvfHu3m9R18TtD8YVnfv5yHT8ZW+5oFixxkbhjZ2nvB6v+0Fozc/uzTkKR6tNU82ojNapIH8JPF/Pq2fkKBh8wAgiUKGdXwfC8Vu5EmoaW71EjRqFw1k3JWPVrjKf12evuyZ6jJxRPp5CTkysMC7P4zqXAcDGQ+e8okdab5qBXM/Dh78EqVrn1aMOieYREoRvUJeiDmTdlKz3EoIGz0jVsPREZ7qMj4TYKM7C4XYHg8Kyy7h0pQmdzL5/11i87YSXcacrfP5TYnAVkHN1Dc7P7iU4g5DLl0kv88xg6g6T4xsWCOfVimDxASOIQIUiXDow4uYkREcZvQqyCW+UtLhzSQWlQ6OFqGlo4Y0ICQkbMcQKyNkU0tZj0urQXAWcnmNXgqE7zF9RnFCIDgWLDxhBBCoU4dKBCKMBM398o97LCHiMBmBojwS33x0srxaM6gAdheKu37LlRJrk7HVCESElhqdCnlJc5qpK0qtSN8OCM//FluLzKCy7rPuoGC3xVxQnFKJDweIDRhCBCkW4dGJ8fxve21eh9zICGgcDHD5b4xYVkfstu6XNgd9++rVgpCnREoWFP+kPW9x1jy9Aml8XX0RI7rd8JW39SnyZpG6GrjVu4dSB5q8oTihEh6RYnBgNHZFfgiC8oQiXTgxLT0R0FN1+MQrOVLlFWuR8y95RUokRS75EdYNwRKy6oRVdOpkBAK0OB57K7oWucWZZ6/TcKOV+y1fi5K7E0VvJ6JxgqTFSA39FcUIhOuT6fuPDwQBPrg+P9wpByIUiXDriCKNUjVJW7Trj/O8UazQWTs6QFNXhc6Pn48n17u7strhozM/ujdb2dkkdjZ4bpZRogGtkTWlbv9wOQKHORz6CpcZIDYb2SECiJYpXpCt1cw8ml3ghcjJTsPr+wZi7oQhCH1/h8F4hCLlQiEUn9n97GS3tJLjkYK9rwpPrj2DqwA4RwRfVWTi5HxZvk1ew7jkK52J9E97YeQoZKXGKBvGKRZ8MAF65OxO2uGhcutKRklRaKyW3A1DJnMlgqDHylR0llbjjtV2CYgtQ5uYeyPMF5ZJgMQuKrXB4rxCEEijCpQM7Sirx3D+/1nsZQQcbafnsaCVW3z8Yi7ed4IzqWGNMPncjstdavO0EFk7OwJPr5Rt3CkWfpg5M8Vq/L7VScjsAPTsfT1+86hZN5COQa4x8gc+bzBVffcOC0Y+Mi1CoRyMIPSDBpTFSPtgJfthvzwkWM/bmjuV0295SfF7Va1XWXcPq+4dg8Tb5GyWXpYPU4dv+xlWkFZZdliS4ArnGSClSLDySLCb859kxMEX6lhQIhfmCoVCPRhB6QIJLQ3zxZiLcuXSliTeqo/YH/eJtJ36oH+uHBItZ9kbpuk65w7e1IlRqjJQgxcLjckOLV8esUoLBj0yIcH6vEIQvUA2XhijxZiK4ERJVSjrxxOioHytC3bUWNy8suQSqH1Mo1RjJhVJk8gjn9wpB+AIJLg3JK7XrvYSAJyE2SlAoCZmDsghtCEpRa+xNIG/uQmOE2DQnOx4plExRKUUmHynvFYIg3KGUoka0OxhsLpY2iiWcqRFxkQekfXsel2HDU9m98F5BhVcHolL4TE7bHYzkmpxA2dz51ixUY+Svgc56QykyZYRCPRpBaAkJLo04WF6NanJg9gmjAVg1U/zbM5cwiI+Jwqheyfiqohr2+mbn71N+6Bj86KvvRUcGsbhGn+SKECn+XEkWk9dIIzURWzNXjRFfs0dlXRMe++AI1mgU1ZAjbqUi5E1GKTJhgr0ejSC0hASXRlD9h+84GCDBYvL6vesmXFHViDd2nvISBnXXWrHtWCVW3z8ECRaT24YNAFtkRB/Z6JOYCJmf3Qtzx/Zy26ilGI9ebmjBHa/t8kvkiG/Nrh2SnlGLoT0SRJs9nvvka78X+vszwhYqlg0EQQQuBoZhgr8Iw4/U19fDarWirq4OcXFxis9TWHYZM9ftV3Fl4cnKGYNw96Buzp+5NmE+2NTQ3tyxbsJAzt8m5YfnA8CoZfmi17XFRWPRVO8NW2zd7OrUrIdhOySFrmmNjUJ0ZATs9dePEXJed2V+dm/My+7l8xr50plCdipqRdj8EUEjCEIf1Nq/1YIiXBohJZVEiONa2yTX00yNQdNTB3ak3ArLLksSefZ6bm+tnMwUjO3bFSOW7OQUM/6wiJDSIdmRVnVfjxSxBQDv7SvH3LE9Fa+VL4LVMTnghCYRNkqRBT4kiolghboUNULK4FdCmPiYKGcKsN3BYNFnxxWJV18GTa/dXY4dJZWyU8Rc3Y2Hz9YIihm1LSL8ndaubWxVvFZWPHsKQntdE55YXyQqbmsbW7EqX9y4lQhudpRUYtSyfMxctx/zNhZj5rr9GLUsn4ZlE0EBCS4N6Rj8OkRVf6hw4uGRac5vsqvyz7gVv8uBb9C01L/Ly5+XIrmTWfL1+IST1hYRWtgaFJypkm0VIWQILOdM7+0rDwmbCoIbIVH++AdHSHQRAQ8JLo2xxkRRSlEBncwRmDu2oz5oR0klVuw8JfscUgZNi8GKp9ILdYiPiZJ1faWRtaorzW5CQqkXlj8MYT1ZteuM7IiDWobAvkTYiMBGiij31SOPIPwNCS6N2fdtld5LCEqW//QWRBgNzg9euUgdNC1VRL26/RvZ/l5KI2uLt51wihhfUir+MITlQm7EQc1Up5JzhaKZa6ghdULDiryT9DckAhYSXBpzoeaa3ksIOsyRRkz4oeBcaTREyAGb3XCb2xx4ckxPn9friZTImpgAsv9gNfGYjykVXofwODPiBVz+DYDg467IjTiomeqUey6qCfIdLQSrVCG9alcZ/Q2JgIW6FDUmNT5G7yUEHc1tDuz/9jJG9kyGvU6+YE20ROE/z46BKdL7+wVXZ5zBAKhlliI1siZmbSG0HM+ORgC8XVztDgbWGBN+k9MX1VebkWgxwWaNwbD0ROSV2gXNP5feOwD44TpiopevI5QLKU7vXePMaGpz8JrTKnGDF/Ike+yDI/jfkWkYl2Hz6oLzZ5dcsHXgaTV9QK6QdvWVIw81IlAgHy4R1PbxKDhdhVnvHFBhZcHBk3fejKgIA9b8pwxNbcrfanPH9ERmtzj8etMxXG1uk/38DXNGSHZP94VEi8ltooDUzafdweD9gnIs3nbCp+vPz+6FjYe+49wAAW+x5Lk+KRtou4PBiryTWLWrTHQ9nr5pfLB/C4Bb7L31wBAA4Px7KfEsE/Mkc8X19ftTYATb6CS+///400NOjq0On+8eET4Emg8XCS4R1P6DbS2+gLkbi1RYWXBgMgItDt/PMywtAYcqahSLI8+NX86GK4cV0wfCZo1RFKHYUnwe8zYWq7oeALyO9uxjgPvmKCXKItUslkvo8iFFcKglSuSY3bKv/JHb07F2d7lfBIaW4kUNpJjoqi12+ES5GHLeg0RoEWiCi1KKGtLuYPDi5yV6L0NT1BBbAHCwosan57umJNhoktpiCwBs1hhk3ZzkFC1bj12QLLz8ZdsgJx0ZYTRIMv/0x8BnKcOQ1RqYLKe4nn196/Z4iy32cV9MasU68NQ2wFUDqUXsUlLKUpGafveExqoRgQIJLg3pGGAtr7ON8A3PjV/OKCCl15EbhWHFmb2+SfIYHTVRsjn6a+CzFLGnhhu8EnErVAvui8DQQ7z4itYeciyugrvgTBVW7RI3u9XCf44gpEBdihpC37T0gd34+YwT1YABsHByP2fhudROQtcuufkfFfOKLS3iGnLfn7wdjwIdoYGCvzzJlPx/XC/x4gtSRYw/xA4ruOeP6y34N+TrDiYIvaAIl4bQNy1tMQD41V09MS7DJpi2UYvfbS1FU5tD0Jzx+U9LMLZvV5gijdhRUonHfqhJESPRYsKUgSn4x+HzipoGpKDk/elLik/PjjyhCJ0vKLmHeooXpfgjpSwXf0VZCcJfUNG8CGoW3fmrUJsQJsUajRk/vlGRO70/SLRE4ZW7M/HbzSWCNgeJFhOmDkzBlqMX/J5mTLKYULjgLk7rDH/ga/G7WmJNTorZ+INdiJDAUFIkLtaBF6jddlI6S7WIcgZbdyehHYFWNE+CSwS1/2BLtpfi7d3lKqyMkIqaEYxgxPX1C90LNTYpKULI1448tTdYds07S+14p6DC63HPLkVAfYERKOJFLoEidoLNv4zQBhJcQYaafzA5KSSCUIpn4b2QD5crvm7uUj28xKK8KQLRHH/bJ4i9Bq7H2YjlpFtSFV9XyrUDFRI7RKBCgivIUOsP1u5gMHLpl7DXN6u4OoK4Dpt6+s+zY3D4bA3nBtjS5sCIJTsFi/OVpK+kCiFf/LuUeD8pEQNiz9l+rBIvbClRZHArBokXglCPQBNcVDSvER1t/yS2CP/BoKNI2BRp5LUPOHy2RrAeTIkFgRwfKamddnmldq/ry7VPUBoxErKd2FFSiSfXc48DUmOUjBqWFwRBBCZkC6ERSmYAEoHFPYNS8eEvhsMWZxYd8pxoMWm5NOCH67KzFPnwhwWBHCEktdNuS/EFryHIctbOZwEiZ9C3J2LCEpA+sJsgiPCDBJdGuKYfiOCkU3QUjAYDXvxJfwDe3liuQ573L7gLiZYoTddX29iKg+XVgsf4w4JAjhAalp4o6b5cbmjxei1S15TcyewXYSRHWBIEQXhCgksjEjuZ9V4C4SN/338WM9ftx+JtpXjk9nRBw09TpBG/v2cADOAXZp7/rQZikVQxw08lZpFyRFyE0YB7JAyzBryFnNS1g4FfhFEwGpQSBBE4kODSCFtc4JgWEr5hr2vC2t3lWDi5HzbMGYGVMwZhw5wR2Js71q1+R8iJfc0DQ7CG4zFfEYuksmaRAL8QlGsWKVfEZYukPVk8hZzUtVc1SKuVlCuMgtGglCCIwIGK5jWCTaXQLMXAIspoQDvDCM7J84QtBF+87YRoN5+YE/u4DBve3VuOV7ef8O2F/ICUSCrfEGCbzE471466GT/ujhU7T/P6fLmKOF9cyqWsvbDssqT1yxVGgeCuThBE8EKCSyMijAa8cncmnlhfpPdSCBdaHQxWzRiEAxXV+Pv+c5KfJ6ebT6jzLMJoQEaqeu3KUiOpvozkAbg9o+Jjo9DS5kBjS7vbsdZY95otX0eyiK3dX8KIRskQBOELlFLUkEm3pGJMnxv0XgbhwSvbT2BID2VRCTXqdaquqmMXIrf2ihWCdw/qhqybk2SJLa4OwNrGVi+xxf7+MY/OQF8HXwut3R9pU7XWTRBE+EIRLo35xeibsOvkf/VeBuGCvb4ZBaerFD2XKy0l17xSamprYqYNX5TYvX6vRnRF6pp9GQL+3CdfY1yGzXleX6NsQqiVNuU7t7/WTYQWZGRLuEKCS2vIoicg+ceR7xEfG4W6xlZJfyK+tJQSs02pKbBV9w9BXqldloiQOttQ6prFrBGEqG1sxar8M5iX3cv5O38affpTGJFBKSFGsI5qIvwHjfYRQe3RAFuKz2PexmLfFxakWEwRaOBIOwUC5kgjmtscosfxze3zZc6fnOHFUr81S/nAl7tmX9+/8bFROPzCOPqWT4Q0/p75SUgj0Eb7UA2XxoRryzj7QROoYguAJLEFcNfr+OpCLqc2SErtlRSndSVr9vX9K8WclSCCGZpIQPBBKUWNGZaeCGt0JOqa2vReiqaEwkfLw7f1wPj+KZwRJblz/rhQKwUmdbZhZ3OU7DWLpT+lQMagRCijxmcBEZpQhEtj8krtkiMpoUAoZY62HL3AK4B8dSFvdzAoLLuMrccuAAB+ckuqV/SKPWZL8XkUll3m/YYs9QO/8FtpjQKuaxbqAJRKuEZ5ifCAJhIQfFCES0N2lFTisR/qdEKdKKMBdw9KxT+OnNd7KapR3dDK+63UFxdyqbVWXMcsnJyBBIvJLSIm/YNcmmTyXDNfB2CKNRp117itIdiraWEMSp1hhJ7QRAKCDxJcGsGmecKFVgcTUmKLhU/MKDXb5CuuZWut3npgCABwHlNZ14Qn1rsL+BRrNGb8uLuk15J1cxL+eeR7xY7vXOnPvFK7YLGwv41BqTOM0BuaSEDwQSlFjfClnZ4IHPi+lSox25RSXLvos+NY9Jl03yt7XRNW7DyN+Ngo0dmGI25K8skglKt4n41+pehgDCqlUYAg/I0/jXeJ4IYiXBpB+frgR8zJXa7ZppRaK3u9PBd6tiieRWwEjT8MQvUwBpXaKOBqvEoQ/sKfxrtE8EKCSyMoXx/cGCDtW6kcseEvEc6gw35hfnZvbDx0TvQD3x8CSWtjUOoMIwINmkhAeEKCSyOGpSfCFhcNez1FutRmTJ8bMKh7AjYcPOfT/U2xRuMnt9jwzyPnUd3Q6vZ7Od9KpYoNf4vwtORY7M0dK+kDP9id08O9M4waBQKTYP//FaEuJLg0IsJowMxhN2LFzlN6LyWkiDQakJYUi07mCDw7vjdqr7Xi+5pGrD/4naj9hgHAU9m9kZYc67ZJPTcxw6fNS+rmJ6W4tmucGYABF+vl+1516RzN+YEfiptzOHeGUaMAQQQHJLg0JC05Vu8lhBxtDgbv7Tvr9rv4mChRsWU0AKtmDsGkW7w3JF++lcrZ/Nji2sc/OMJba7Voan8A4DyGD6EuqFDdnMO1M0xKl2sw/10JIpSgLkUNCcVv14FI7bVW0WMcDFBZd03URBSQbjiqpEtuXIYNT2X3gjUmyu33rl19bAFu1zhp7x8G3PVmrA9cKHbxhWNnGI2QIYjggiJcGjIsPRGdzBG42hy48wTDicXbTjj/my/KIzUipKRLjuvc8TFReHhkOuaO7ekmDnIyU9DZHIVZ7xwQfV3zs3t5vY52B4PnPvma8/hQ6eILt84wahQgiOBCdoRr9+7dmDJlClJTU2EwGLB582a3xz/55BOMHz8eSUlJMBgMKC4u9jpHWVkZ7rnnHtxwww2Ii4vD9OnTcfHiRbdj0tLSYDAY3P4tXbrU7Zhjx45h9OjRiI6ORvfu3bF8+XKva23atAl9+/ZFdHQ0BgwYgO3bt8t9yaoSGUFBxUCEK8ojJ2IlZ/MTOnfdtVa8sfMU8krtXueoapBmEZGWbPH63ar8M6ht5I/8ea4vWMnJTMHe3LHYMGcEVs4YhA1zRmBv7tiQE1sANQoQRLAhe/dvaGjAwIEDsXr1at7HR40ahWXLlvE+Pn78eBgMBuTn56OgoAAtLS2YMmUKHA73upvf/e53qKysdP775S9/6Xysvr4e48ePR48ePXD48GG89tprWLRoEdauXes8Zt++fZg5cyZmz56NoqIiTJs2DdOmTUNJSYncl60KB8urBTc9Qj88UzBy0zVyNj+lqSClheHtDgbvFZRLXl+ww2XIGoqEc6MAQQQjslOKEydOxMSJE3kff/DBBwEAFRUVnI8XFBSgoqICRUVFiIuLAwD89a9/RUJCAvLz85Gdne08tnPnzrDZbJzn+fDDD9HS0oJ3330XJpMJ/fv3R3FxMf74xz/ikUceAQCsXLkSOTk5ePbZZwEAixcvRl5eHlatWoU1a9bIfek+EwqbWSjjGeWRk66Rs/kpTQUpLQw/WF4tqa6NXR8RHIRrowBBBCua57eam5thMBhgNpudv4uOjobRaMTevXvdjl26dCmSkpIwePBgvPbaa2hra3M+VlhYiNtvvx0mk8n5uwkTJuDkyZOoqalxHuMq4NhjCgsLBddXX1/v9k8taDMLDi5daZKdrmE3P7FxOnKGS+/0SCsqLQyXer34mChVNmepTQaEb4RjowBBBDOaC64RI0bAYrEgNzcXjY2NaGhowK9//Wu0t7ejsvJ6TcyvfvUrbNy4Ebt27cKjjz6K3//+9/jNb37jfNxut6Nr165u52Z/ttvtgsewj3OxZMkSWK1W57/u3aUNApbCsPRExJoiVDsfIYzF7H6vY6Okvd27dI6Wna6Rs/lJPfc7BRVenYNsYbhNxqxCqdd7eGSaz5vzjpJKjFqWj5nr9mPexmLMXLcfo5blB3UHZCCj5P1AEIQ+aN6leMMNN2DTpk14/PHH8ac//QlGoxEzZ87EkCFDYDRe3xCffvpp53/fcsstMJlMePTRR7FkyRK36JjaLFiwwO3a9fX1qomuCKMBkzJt+MeR86qcjxDmlWkDYIuLxs5SOz4tdneP58IzBSM3XSO1S46NhkkZZs7VOSh3ZIhY6gkAEmKjMHdsL9H1CEGeUPpAI2QIIjjQxRZi/PjxKCsrQ1VVFSIjIxEfHw+bzYabbrqJ9znDhw9HW1sbKioq0KdPH9hsNq/ORvZntu6L7xi+ujAAMJvNfhV0d/TpQoJLI2xx0ai71oJ3CypEDUO5UjBipqRc6Ropm1+E0YCFk/vhifVFoq+Bq5ZLrlO8kMEq+3qW3DvApw2ahkfrC42QIYjAR1ePguTkZMTHxyM/Px+XLl3C1KlTeY8tLi6G0WhEly5dAABZWVnYvXs3WluvRy3y8vLQp08fJCQkOI/58ssv3c6Tl5eHrKwsP7wacdodDF518X4i/ANbLzW0RwKvCPCEKwWjNF3Dbn4/uSUVALD12AWvWqYEi3RR71qDtaOkEiOXuqfsRi4VT9nxvZYUlVJPcm0xCIIgwg3ZEa6rV6/izJkzzp/Ly8tRXFyMxMRE3Hjjjaiursa5c+dw4cIFAMDJkycBdESb2MjSe++9h379+uGGG25AYWEh5s2bh/nz56NPnz4AOordDxw4gDFjxqBz584oLCzE/Pnz8cADDzjF1P3334+XX34Zs2fPRm5uLkpKSrBy5UqsWLHCubZ58+bhjjvuwOuvv47Jkydj48aN+Oqrr9ysI7TkYHk1Da+WweyRaYiLMeGNnackzxF0jT4dPlsjKW23cHI//HxkOmfkRWm6RswwVU7HamKsCQWnq/DBgQp8UXLR63F7fRMe++AI1ogIJ3+mnsgTKrBRa35mKM7hJAitkC24vvrqK4wZM8b5M1vv9NBDD+H999/HZ599hocfftj5+IwZMwAAL730EhYtWgSgQ4QtWLAA1dXVSEtLw/PPP4/58+c7n2M2m7Fx40YsWrQIzc3NSE9Px/z5891qq6xWK/71r3/hySefxNChQ5GcnIwXX3zRaQkBALfddhvWr1+PF154Ab/97W/Rq1cvbN68GZmZmXJftiqQ2JKGLc6MmcNuRFqyBV06R+MXo9Owbk+FtOe6iJotxdJSt8mdzaIpOTnpGim1THI6Vh/74Cs0tAjPhgSABZ98LZqy81fqiTyh/IevIket+ZmhOoeTILTCwDAM9WwLUF9fD6vVirq6OqdvmFLW7f4Wr26nlCIf8TFRGNUrGV9V1LiJU6OhY/ahENaYSLw5ayhG3HTd6LKw7DJmrtsvet0Nc0bIFiF8m2C7g8GoZfm8kTW22P75if0wd6N4DZdcPvzFcIzsmaz6ecVgX7dYk8He3LEUEZGBryKHT/yzfwGp6WS1zkMQWqLm/q0GNEtRQ2oapY1mCVdqr7Vi6zHvWiQpNk5119pgNBjcNnN/GUMKbYLWGJOkWqYXPz8u65pSKSy7rIvgEirMJ08oZfja9alWIwM1RBCEOtBgPw0xGuh2+xPP+iB/GEOKzVfkmoHIRXVDi+RrykO/gDV5QqmH0vFPrqjVyEANEQShDhTh0pCsm5OwatcZ8QMJRXDVB0n1xpKClG/6W4ovyF+4imTdpH10yxW9PKFCrZhb6fgnV9RqZKCGCIJQBxJcGjLipiTEx0bRAGs/YIsz86YGx2XY0NkchcJvqwB0FI271noJ4bqRV11pFt0ELze0INEShZqGVs1jTfGxURgRAF5MbGE+e++2HrvgVxEUisXcaogctRoZqCFCHqEm/gn1IMGlIRFGA5beOwCPfXBE76WEHC/+hDs1yLUZ//PI95I2Y67nSuGeQd3wbkGFrOd4kmKNhsPhwMUr0lOPS300L1UTrURQqLrbqyFy1KphpCHZ0glF8U+oBxUVacw/j3yv9xJCkgSL2Wto8vZjFwTrrYTMQvlqtaSQnWHDWw8MQaIlSvZzWSZl2nD/8B6SjrXFmUU9uLSAvf+LPz+OxxTed7nX87XOKVCRMwydD7VqGGlItjTE6jtpnihBES4NudbSjrzSS3ovIyTZWWrH0x8Xu33YGQ3cJeRinVVCG7kQrt/0I4wGXGt1YP5HxTLP0sE7P0TI4mOj0NjSjpY2fh+uF3/SX3exJSUaqHZHmxp1ToGKWl2fatUwqlkLGYpQJychBRJcGvL77aV6L0E1WHPSyw0t+FvhWdXOO3mADUfO1cqOLL3DkcITCmwIbcZiGzkXXJugLc73mpa6RuFaMAOAxdtKMSFTvw9yvrQeF2qKoFAv5lZTLKnRyEBDsvkJZfFPqAcJLg35tqpB7yWowvOT+iEjJQ5VDc3oZFb3LVRw5jJ+f08mEixmXLrShOROZjzzcTEu1jfzbuhcA5mlwrUZK9mguTZBsdoXKYg9z5cPcjWKe5VGA9UQQeFQzK2WyFFrwgANyeYm1MU/oQ4kuDQkJio0SuZW7DyFxpZ2v5y79lornlhfhPnZvTB3bC9EGA1YNLU/Z2qFxZcKHa7NWO4GzTeLUSgtpDZyP8jVKu5VEg0E1BFB4VLMTSIn8AkH8U/4TmgogCCh5w2d9V6CKvhLbLmyYudpjFyajx0llc7UijVWeRE6F/GxUZybMbuRS0VoFiOfGajauH6QezYPeBaNq1ncK1foSSn2lgoVcxOBghpNDkToQ4JLQyIj6HbLwV5/XQCMy7AhOjJC1fPzfTi6buRSEPvWmpOZgr25Y7FhzgisnDEIH/5iOGxxZt7ry8Hzg3xHSSVGLcvHzHX7MW9jMWau249Ry/KdIkrtzj4539j9IYLI3Z4IBEj8E1KglKKGRJLeUsTLn5eisznKbaC1GtQ0tmJF3kmM7HmDV11MTmYK3rx/MOZuKOItvvdMWQnVRHmmhcTSpFLw/CCX4kklddaj1JowOXVq/upoo2JuIhCgTk5CDAPDMMFnUqMhak0bb3cwuG3JTllGlsR15o65Gat2lfnt/Hz1S9uPVeKJ9d5GtexWzkZRlNRESbFSYK/zyO3p+OxoJe/52x0MRi3L5z0XKw4n9O+K9/eJd5WunDEIdw/qJnoc+zoe/8HM19O+gAHwvyPTMC7DFtIiiNzFCRbP98LQHgk4fLaG3hs6oNb+rRYkuERQ6w9WWHYZM9ftV3Fl4cXcMT39OofSU0C5Iiam+CJLQudkcf1wrqhqxIaD59wieZ6iim9TV/v9tWHOCFmF2r4U4QeLWOFbJ7mLE3zQe0NfSHAFGWr9wbYUn8e8jcXqLSwISYiNws9vS8eKnadkP/fDXwzHrzcdFe1I+8P/DERVQzOSLWY8s+morDQke469uWM5zVC5NlupkSWuc3KhVHyo+f5KkbFeV9i12+ubUH21GYkWE2zWGMHXECwbEt86pw5Mwdrd5YrENhHa+PJFjFCHQBNcVMOlEeHcDhwfE4WHR6Zh7theAICNh85J9qZiBcuIm5IELRYYAAsnZ2Bkr2Tn7178SQZnOpAPofolvtZ8tQ0PlVoAqPn+UlrcG2E0oO5aC5bv+EaSgAqWOYhC63x7dznnc8hdPLwh53mCCyrj1ohh6YmwxZn1XoZmdDJHYmJmV3w4ezgOLxyHuWN74WB5NbYeu4AZP75R1rlYAZCTmYJHbk+HgefzafG2UjdLgwSLSdHa5VgdyDE8FLNr8AWxtnSpzB6ZpljkyLGb8LVb0p/30vM6Yuvkw1VsE+GFnC9iRPhAES6NYA08H/tAesQlmLna3IYvSi6i+Ls6TB2Y4lXwHR1lRFMr/3xAlqeyezsFwI6SSs70DUulR2REqauznGiR1GMrqhq9Uo+JFhNeuTsTk27xvcbJ1WTVF7IzbIqeJ/cbvS+RQS3TkEqNXV0hd/Hwg5znCS5IcGnIuAwbTJFGwUHEoUYlT9pFitgCgLTkWADyRsiwG7vcNJsSZ3IpbufW2Ci8sfOU1+PVDS14Yv0RPPp9OhZM4vb9kiMu2AggX5pLCF9d2eUKKKUbklga8qns3khLjlWt+F7LEURE6EDO8wQXlFLUkH2nq8JKbKkB+4EkNdLgurHLSbMpNScUMzxkhYGQUHx7dzm2H7vg9Xu5jvDtDgafHZXuEu+6TsA3Y0a5AkrJhiQWRWPQMXaKy/BVKb5siOQuHr6Q8zzBBQkuDfnzrtN6LyHoqGno8C2TG2m4dKVJUAx54oszuZDb+fzsXqhtbBU9xwtbStzqkJTUOClNf6nhyi5XQCnZkOS+PiWjijxRWhtH7uLhDTnPE1yQ4NKIdgeDo9/X6b2MoGPxtg5RITfSwB7PK4bizHjqrl6YO6Yn5o65GX/4n4EYp7B+ib2O6/ieDXNGYG/uWKQlWyQ9v7qh1a2AVknRrZL0V6IlCv95dozPtU9yBZSSDUnu61MyqsgTsXUaADx6e7rX7E0aLUTQ2CnCE6rh0oiD5dVopnSibDzTg2IRDq5aJK7RLzUNzVi87YTzfKt2lUkqvJYzvgeQl5JyFRRKapyUpL+qG1px+GyNIisKV1yL9j1tO/gElNxRKEpen1xbDi6krPM3Of2CwryV0BYaO0W4QoJLI6gbRTmu6UGugmlPuEL1rmJoR0klnlxfJNv/SUl33LD0RCRaTKhuEB/p5CoolNQ4yZlr6Ipa700ls+TkbEhKXx/g+2sUW6dS/zQi9KH3BsFCgksjqBtFOZ7pwUWflXI6yEuNUCkxJOTrjqusa8JjHxzB7JFpyOaYF5hXaofDIR7Z9KxXktL96BnJE4oyCaHme1PJN3qpG5LS1weo8xpp4yQIwheohksjhvZI8NmUMhzpZI7A0B4JHr9132qtMZGYn90Le3PHitZFKKmNkmJJ8U5BhVdn3PZjF/DYB0dQe61NcE0GAAsnd6SkWCNPALJqnFgj0OY2B57K7oWuEkx2/dUpxQqTuwd1Q9bNSaqmT/jqYoRIiI2ibjCCIHSHIlwacfhsjew0CAFcbW7HHa/tcooPrihT/bU2vLHzNPrYOosKLiW1UXK649i05C9Gp+OdveJ+WOw8Ptd6Mvb3L03JkJSi40p12uKiMT+7N+quteDdggrJdVXBgGsUzV7fhBc2f42G5nbe4+n/dwRBBAIkuDSCariUw4oYa2yUz7PJpKaWki1mFJZdxqUrTTh98YrktbLrW7dHmvnofbd2x8ovTwvWk+3NHeuWohvaIwGHz9ZgS/F5VFQ1cpqqXqxvwhs7T+GtB4ZgWHqirLqqYICNohWWXRYUWwBQ29jqU9E8QRCEGpDg0ohgq+EyRxjQ3B4YsQF2FUJ+VlK70aQ6wz+z6ShnnZjavL+vQpKIdC34v+O1XaIRN9fn780dG7KdUjRChSCIYIFquDRiWHoiTBHBs8EFitiSi73umuBQYynO8LWNrZqILQCovSZNRAL8zvNSnu/Puio9oREqBEEECxTh0pLQ2OMCmsXbTrhZMHB1LuZkpmD1/YPxwpYSVDdcFzxd48xoanNIcoZXg/iYKEHBxVJwpgpDeyRIniXpSShHd5R0cxIEQegBRbg04mB5NVragjNqFEx4+l1xjXfZUVL5gzC7LnYSLSbM+PGNmoktABjVK1nScat2ncGIJV8qGtsDqBfdYTsh+aKHekAjVAiCCBYowqURoRxl0AqLOUK0QNoTz1qovFI7Z6djTUML3vhS2qzLuWN6orGljbf7T0yGGA3An+4bhFe2fyP1ZUgyTvVEzeiOEtNXrVBiuEoQBKE1JLg0gmpIfCfSaEB8bJTsKBRby7T/28uiA6GlMLJnMrJuTuLt/ps6MAVrd5fznnfVzCFIsJj8WiemZnSHz/SVy5lfaPSRP6ERKgRBBDokuDTC27yTkEudiIGoGPvOVClOywHeESOhTX7wjQmCEaEtxed9ei1iqBXdkePMn1dq1zUKRk7wBEEEMiS4NOJQRbX4QYRfeX9fhc/n8IwY8W3yYhEXf0U8f5bVAxMzU1SL7kh15l+Vf4bTD0xsPiVBEES4QIJLI/aVVem9hLCnoUVe/ZcrRkNHKlCOaBCKuPgyiFmIiZkpqkZ5pNYevldQ7rMpLUEQRChDXYoacb7mmt5LCCis0e5a3xZnhjWGX/8b0DETTy8cDJBgMal2PqHuOiX4ay6i1EicHD8xgiCIcIQEl0akxlPRvCt1TW2Yn90bK2cMwoe/GI4ZP74RrTxmq6wguXdwN+0WyAFftEepXYKcQcwGnv92/dkf9gdsJI7vrAZ0+IlJgTp1CYIIZyilqBFZ6cl489/f6r2MgGLjoXNYOLkfnlx/RLDz0BobhaX3DoA1xoR3Ciq0W6AHXNGeHSWVWPRZqVvHoS0uGoumSisU56r1yv/Gjnf2VsBVtxkMwCOj0zmL8f1pf8BG4h7/4AjvAOyHR6ZhxU5xSw3q1CUIIpwhwaURxiAa66MVlXVNeGJ9kehxMVERGJdhA9BhUKrEk8oX+PysdpRU4rEPjngdb69vwmMfHMEaiYXirrVeO0oq8Zc93vMVHQywdnc53nogwWuYtb/tD8R8rsZl2LDx0Hdh5/aulwUGQRDBCQkujbh0pVnvJQQtrkOppw1KxbsaRrnYqM7EzI4oFLuptjsYPPfJ14LPXfDJ17IKxYUsGFg8h1nLRalIEOu6FIuChZrbeyAbwRIEEZiQ4NKI6qskuHyBrf8Zl2HTVHCx4uHdggq8W1Dh3FQ7R4sbsNY0tmL/t5cxsqe0ET77v70syYJhRd5JjOx5g5vgkSKkfBUJQl2X4eT2LscIliAIgoUEl0YkqtjhFo6w9T9sEbcvBqa+wG6qOZk2SccXlkkTXDtKKvHcP4UjZiyrdpVh1a4yp1gCICqktBAJ4eD2LscINpReN0EQvkNdihphs8bovYSgxdXugC3iNkAdOwW5sBvt7lP/lfkMflgxJGStwIW9rqNW7LEPjngJUNeh3WIiAegQCWoMo2ajYHcP6oasm5NCTnRINYIlCwyCIDwhwaURw9ITER1FtxvoMBGdMzpd0G7AlUk/1E+xgoDPTqGTOQKxpgi336VYo71+5ysMpJuoZt0kHN2SUrcltA6xx17+vBT7TguPNCKRIB2p1hZkgUEQhCekADQiwmjAI6PT9V5GQOBggL/sKcfUgSmShMY7BRWYuW4/Ri3Lx46SSgAdomtv7lhsmDMC/zsyDYkWE642t6PRQwg1trR5/U4txIRcfGwURogUt4tFTHyBFVKPfXhY0vEkEsSRam1BFhgEQXhCgktD5o7trfcSAorPjlYKust74pomAzpEbN21FrxXUMFrFeHrwGshHr39ZsHHl947QDSlpoXIkRqNI5EgjhQjWH84/hMEEfyQ4NKQw2dr9F5CwMBGX+QIIs96I1/Scb7Abqpzx/bEmgeGwBZndnvcFmeW7MEVCCKHRIJ0hEYyhaoFBkEQ6kBdihpyoaZR7yUEPZ71Rlp3K3puqr525okNsTagw2VehXp2QeSKhHA2/QwnCwyCINSDBJeGFH1Xq/cSQgY1U3FC7vVGD7HDtakK+VOJIWV0zpzR6Vi7uxzgeJzh+G85xMdEYelPB8gSCWT6GR4WGARBqAsJLg25SEXJbqRYo8EwDOz18k1h1UjFsSNn/vPsGBw+W4NLV5qQ3MkMMEBVQzO6dI7G0B4Jzse4NlW+SI+cCJCUiInQDEXA24erkzkCV5vFa7dWzxoi2ZgVINNPV3wR2gRBhB8kuDTEYqLb7QorFrjmEfLhOZdPKB0ndh52DaZIo+DGyfcYX6Rn6sAUfHa0UlYESCxiIvb4uAwbVuWfxnsFFai91ioqttj7OOIm6YKBTD8JgiCUQ0XzGnL3gFS9lxAwPHVXL1hjTGhuc2B+di9Yo8XFqGf9lGsBsxjxsVFuP9us0V7RmHYHg8Kyy9hSfB6FZZcFjUDZSI9nDVllXRPe3l0uaETKh5hpqNDjeaV2vLHztCTzVKXF3WT6SRAEoRwKuWjImcsNei8hILDGRGLjoe/wxpenZT2Pq36KLx3HwkaWxOpt5NQlKemO9GcESO56lBZ3k+knQRCEckhwach31KUIoMMbS64/VpLFhP88OwamSO+grGu6zV7fhOqrzUi0mGCzxrgJK6HUoJy6JKVmpa4RIDVrf8SGXrPMHdMTI3smKy7uJtNPgiAI5ZDg0pAeibF6L0FXbHFmNLU5UNsob2YgAFxuaMHhszW8QkVqAbNnMfvQHgmy65J8jeCoGQGSM/S6V9dOPgk9KRYWNvLzIgiC4IQEl4Y8mJWGV7ad0NyoMxBItEThtf8ZiAffPaj4HAVnqnxqvedKGwpZQgDcUSlfIzhqRYD4InP+uq4UCwsy/SQIguCGiuY1xBRpRGa3OL2XoQvVDa044GMx9apdZ9zmKUql3cFg5c7TeIyjyF1IbLniGpUSG+/Ch5qO7nLqttS8Lt/gcK4mBIIgCOI6FOHSkO3HKvH1+Xq9l6Ejvsf25Po97SipxKLPjivy+nLFNTokFOnhQ+0IkNw6MjUjT2T6SRAEIR8SXBrR7mDw/GZptTaBhFIHcy6ybkrG+oPfSY4qcSHU7edZn1XT0Iwn1xf5tH6+uqRxGTY8ld3L6XvFwufDpfbYF6l1YPGxUVh6rzwneSmQ6SdBEIQ8SHBpxMHyatQoKBbXGzXEltNk8+YkvHJ3Jp5YL93olG9NnnVVXPVZRoNv6+eLSnFdKz4mCg+PTMfcsT0RYTTgNzn9/BoBklqPtXrmEIzsJd1JniAIgvAPJLg0wl4fnt5EnqJl0i0pePT7dLz9w2xAX2CjPHzF43IHPidaolDdcF0Uc0Wl+K5Vd60Vb+w8hT62TsjJTPF7BEhqx+AIikIRBEEEBCS4NOK/YSq4usaZsWhqfzfRsmBSBgb+KB7P/vMYGiTM++Mj2WJWZELKRZLFhL25Y1H8Xa3g3MRAGW1DHYPqIGfmJUEQhC+Q4NKI4xfq9F6CLvzvbemc9UOTbknFhMwUt/l/LJYoIxpaHeInNyg3IfXkckMLxr7+b7w0JQN3D+rGeYyc0TZa1DdJGXpN8CNnugBBEISvyLaF2L17N6ZMmYLU1FQYDAZs3rzZ7fFPPvkE48ePR1JSEgwGA4qLi73OUVZWhnvuuQc33HAD4uLiMH36dFy8eNHtmOrqasyaNQtxcXGIj4/H7NmzcfXqVbdjjh07htGjRyM6Ohrdu3fH8uXLva61adMm9O3bF9HR0RgwYAC2b98u9yWrwvnawIxwGfz8ZX5byQXexyKMBszL7o3DC8dhfnZvxMd0zDuUJLYAVF1tVtVEVGzeodRr7Sy1q7YmMXIyU7A3dyw2zBmBlTMGYcOcEdibO5YEgwh8szClzLwkCIJQgmzB1dDQgIEDB2L16tW8j48aNQrLli3jfXz8+PEwGAzIz89HQUEBWlpaMGXKFDgc1zfaWbNm4fjx48jLy8PWrVuxe/duPPLII87H6+vrMX78ePTo0QOHDx/Ga6+9hkWLFmHt2rXOY/bt24eZM2di9uzZKCoqwrRp0zBt2jSUlJTIfdk+86P4wBx3wvDk4tTSYScvXhUcAg2wg5dPSRq87EqXztGqjpFhV/ny56Wca5Z6rXcKKrBy5ynnEOyWNofkodhKEBt6zSJnOHcoI5YaBvjfAwRBEEoxMAzflivhyQYDPv30U0ybNs3rsYqKCqSnp6OoqAiDBg1y/v5f//oXJk6ciJqaGsTFdZiA1tXVISEhAf/617+QnZ2NEydOICMjA4cOHcKtt94KANixYwcmTZqE77//HqmpqXjrrbfw/PPPw263w2QyAQCee+45bN68Gd988w0A4L777kNDQwO2bt3qvP6IESMwaNAgrFmzRtJrrK+vh9VqRV1dnXO9Sthz6r8+uaxrTYo1GjN+fCNW7Dzl87k+/MVwjLgpibNWpt3BYNSyfFlpQbYgfG/uWADAqGX5vMXjQEe3oty9c8OcEV5pQXatQteScn090laUPrtOYdllzFy3X/Q4rvcAQRDBg1r7t1po7jTf3NwMg8EAs9ns/F10dDSMRiP27t0LACgsLER8fLxTbAFAdnY2jEYjDhw44Dzm9ttvd4otAJgwYQJOnjyJmpoa5zHZ2dlu158wYQIKCwv99vr4uK1nMswcg5cDkURLFP7z7BjMHdtTkaO6J3P+eghDX8nDzHX7MW9jMWau2+90jJdbg+VZEB5hNGDh5H68nXoGAH+6bzASLSaOI/jhSh+yhepyv6F4ij2t01aUPnNHampYzXQ1QRCE5gpgxIgRsFgsyM3NRWNjIxoaGvDrX/8a7e3tqKzs+OC32+3o0qWL2/MiIyORmJgIu93uPKZr165ux7A/ix3DPs5Fc3Mz6uvr3f6pRYwpQrVz+ZPqhlYcPlvjFBiAbynGxlbvgdXsZp8ns94pPjbKzWV+R0klFm87wXksO24mqbNZttkqX/owJzMF/zsyTda5PNEybUXpM2+kpobVTFcTBEFoLrhuuOEGbNq0CZ9//jk6deoEq9WK2tpaDBkyBEaj/hGgJUuWwGq1Ov91795dlfMeLK/2Eh2BDPvtnm92Xoo1Gg/f1kPx+dntfUsxf1E9FwZ0uLwD/JEbloWTO9JlciIVUuYOstf3BdeORhZ/1FjJ6awMF8RmYao5e5IgCIJFF1uI8ePHo6ysDFVVVYiMjER8fDxsNhtuuukmAIDNZsOlS5fcntPW1obq6mrYbDbnMZ6djezPYsewj3OxYMECPP30086f6+vrVRFd9rprPp9DS5ItZhSWXXbWXP3n2TE4fLYGl640IdliBgzAvrIqn67BoMOOwWKOkOzHVd3YivcLyvFgVpqg/5YBwOJtpZiQaZMdqRDzrxIzHZWDq3mrP2qsKH3mDXmYEQShB7qGlJKTkxEfH4/8/HxcunQJU6dOBQBkZWWhtrYWhw8fdh6bn58Ph8OB4cOHO4/ZvXs3WluvR43y8vLQp08fJCQkOI/58ssv3a6Zl5eHrKws3jWZzWbExcW5/VMDX+YHaokBHWm7ZzYddau5uuO1Xai71gJzpBG//sdRzPrLAazeVabKNe+QOXpm8bYTGLFkp+TIjVhEg8UaE4mnsnuJRrDUSrUCHWkrf9ZYUfqMG77ILZuGDrdGAoIg/I/sCNfVq1dx5swZ58/l5eUoLi5GYmIibrzxRlRXV+PcuXO4cKEjVXTy5EkAHdEmNrL03nvvoV+/frjhhhtQWFiIefPmYf78+ejTpw8AoF+/fsjJycGcOXOwZs0atLa2Yu7cuZgxYwZSU1MBAPfffz9efvllzJ49G7m5uSgpKcHKlSuxYsUK59rmzZuHO+64A6+//jomT56MjRs34quvvnKzjtCKxE5m8YMCAAb4IfXpnv6srGvCYx/4NgORj1nD07DvW3kpV9cRPEJcutIkGNFwpe5aG1bsPI2Nh74TjSzxmY5Khe20HNojAXe8tstv7vVSRwCFY/osJzMF4zJs5DRPEIQmyLaF+Pe//40xY8Z4/f6hhx7C+++/j/fffx8PP/yw1+MvvfQSFi1aBKDDvuH9999HdXU10tLS8Nhjj2H+/PkwuLhwVldXY+7cufj8889hNBrx05/+FH/605/QqVMn5zHHjh3Dk08+iUOHDiE5ORm//OUvkZub63bdTZs24YUXXkBFRQV69eqF5cuXY9KkSZJfr1ptpVJb0fWka2cTmtsZzWrNXO0d8krtfhF0rq39XGk7vnUBkBTpcB0NU1HViDd+sNEQ+j+V6/mtMSZJ74sPfzEcRoNBkTBgI2ie65LzOgmCIIKNQLOF8MmHKxxQ6w/W7mDw41d3Bmxq8adDumHaoG6aeoUZAKy+fwgSLCbkldrx8Vff42pzm2rnZsWc5zzEg+XVsNddw+JtJ3j/HnzPF4NL1An5cG0pPo95G4tFzxsfE+VmDCu3vsuXGjGaN0gQRDASaIKLZilqRITRgB6JsQEruP555Dy++Fq7kTQWcwTuH9Ydi7cpS8kJIVT4zLqyF5ZdFvxbKJ2LyJWmGtojwdlw4ClYpNZOebrws/VdUqNTStNnZJhKEAShDiS4NKLdweDUxSt6L0OQxlZpnYJq0NDcjnV7KvxybinDm/3ZvceKOlf4RJvSjkcl9V1c6xKCTUV6rkuu2CMIgiB07lIMJw6WV6OhRTtBE64snNxP0vDmQOne86Xj0Z8eWmSYShAEoS4kuDQinHyO9CS5s1lStCeQzC/5LAriY6MkPd8f7y0yTCUIglAXSilqRLj5HKlN5+hIXGkSL6iXep/ZyBJXZ6Qe5pdcNVYOB4NZ7xwQfa4/3ltkmEoQBKEuJLg0Ylh6IiymCEoryoTtFlwwoQ/mfXxU0Fleqp8U23V3sLwancyRXp2R1tgoLL13gOb1SZ41Vu0ORjcPrUBJuRIEQYQKJLg0IsJowKDuVhSUUQpGDgyAqQNTBMUWi5SIlBQvrroAmXmp5wgaMkwlCIJQF6rh0pDz1Q16LyEo+fv+c4Jiy2jo8PMSi0iJDbt2Rawg3B+DprnQawSNUDG/q9gDoMl9IAiCCHYowqUR7Q4G52qb9V5GUNIokoZ1MECCxSR4jFDXnSdiHlxae1OpNYJGroEp3/gi1nYDAEYtyyePLoIgCAmQ4NKIg+XVoC///kOseFus607qOfXyppLroeWJUpHIJ/bySu3k0UUQBCEDSilqBHVz+Rex4m0l99/znMHqTcWXSmXF0Y6SSsHns2Lv7kHdnKIvGO8DQRCEnpDg0ohw7ObqZI7w+zWk+mXJuf985wxGbyp/iMRgvA8EQRB6Q4JLI4alJyJBopFlqHC1WZoFhtIeOzmdemJGp1LOGYzeVP4QR8F4HwiCIPSGBJdGRBgNGHGT8hqcQCLFGo2Hb+uh2vnuHdJN0fPkdOpJHaEjdM5g9KbyhzgKxvugB1p1shIEERxQ0byGVNY26r0En4iPicLqWUMw4qYkHCyvxnv7zqpy3n8eOY/oKCOaWx2SugjjY6OweuYQ/Dg9EYfP1mBL8Xmfuu4SLVG4Z1A3ZGfYBM8RjN5U/hBHwXgftEbrTlaCIAIfElwa0dLmQPH39XovQzEGAEt/OgAjeyYDEN905dLU6pC0BgBYeu8AXGluxR2v7VKt606KxYKeRqRK8Yc4Csb7oCV6dbISBBHYUEpRI/5eWKH3EhSTEBvltUlITdGpCZvuA6Bq150cYaCXEalSpBqYyhVHwXYftCJYO1kJgvA/FOHSiLPVwZdOjDVF4NHbb8Lcsb04N2S+FJ0/WDi5H34+Mh1Ah9km34ZmQMeGNi7D5rcIi1CUTK65qBaIGZjKFUfsa2xuc+AP/28gwABVDc0B83r1RE6Tgi++agRBBB8kuDSiR2Ks3kuQzbWWdvSxdRati3IVH9uOXcC/Si+pvpbkzmZEGA0oLLuseENTUwxxGZEGct2OWm71Qq+RBAR1cBIEwQ8JLo14MCsNr24/EXRu81KiRa7io+pKs18EF1vUrXRD87cYCoa6HTXc6gP9NeoNdXASBMEH1XBphCnSiDmj0/VehiyUeDQ9mJUGNTNKniakSjY0X53WxQiHup1weI1qIOb3JtWolyCI0IMEl4YsmJSBDFuc3suQzaUrTV6eQi1tDk6PITWFJVdRt9wNTQuhEA7O6+HwGtXAX00KBEEEP5RS1JjBN8ah1B5c9hAVVY0YtSzfbcP1tANwTc8tmNSx4azbU+6WQjUagNmj0mE0AG/vLhe9rmtRt2v91dAeCdh6jD8q5bqhaVHEHA51O+HwGtVC7SYFgiBCAxJcGtLuYPCPIxf0XoZkDACssVF4Y+cprwiR58+VHnU8CyZl4JnxffH3wgqcrW5Ej8RYPJiVBlNkR1B14I/i8cKWElQ3tDrPYYszY+awG5GWbHEr6uaqv+LjkdvT3TY0LYRCONTthMNrVBO1mhQIgggdSHBpCNtKHwy4RrCkJtsYuBfZmyKNmD36JgDeHYITMlMwITNFdEPiK9Tm47OjlfhNTj/nebQQCkrNRQPRQoIPcpeXj69NCgRBhBYkuDQkmNItNms0Zvy4O1bsPC3reVzpOaUdgkL1V0LXf7+gHMmdzejSORpDeyQgxRotGB3ztYhZifN6IFtIcEHu8gRBEL5BRfMaktzJrPcSRJk75mZsmDMCe3PHIi3ZougceaV253/70iEoVn/Fx+JtJzBvYzFmrtuPO17bhcxuwo0KUwem+CwU5Div+7tr0l+QuzxBEIRyKMKlJUHQMZ+REueMTilNs71bUIFh6YkYl2GT1CHI5/OlRkSwsq5JVLR5piGVIqVuR6xrUgunfF+g2iSCIAhlkODSkKqGZr2XIMribScwIbMj4qN0QDUrGjqbo0TFjlCHoFYF2HK6FMXqrsTqdkJh9AvVJhEEQciHBJeGBEMHl+tm71q3IwdWNGz66jtJx9vruQWIUsGnBCnRNDXqrshegSAIIjyhGi4NGZaeCFtc4NdxuW72bN1OilW+WNx8VJoFxpGz1ZzGo6zg0yITm2wxcxq5sqhVd0X2CgRBEOEJRbg0JMJowIs/6Y8n1suLGGmN52afk5kChwN+W/ff95/DzhOXFHXo/e/INHSLj8HibScUXZv1Gntm01G3SFuKh+mqL3VXrmnI5E5m2OLMuFjfrLu9QjDZUhAEQQQ7JLg05nhlnd5L4IXd7If2SEBh2WXnRjy0RwIWbyv167W5BiCzQkdovV+U2PGfZ8fgL3vLRVOPXHYGDIDaxlYArW7Huq7HGmNSXHfFlYaMj41yCjW97BWCzZaCIAgi2KGUooa0Oxis+XeZbtfvZI50Or17wm7vUwem4PbluzBz3X6ntcLw3+9UZM8gB665hlILzA+frRGcX2cA8Ojt6V52Bl3jzIiPjRJdj73umqTX4Fl3xZeGrGvsEHdWj2trZa8QrLYUBEEQwQxFuDRkf9lltOtkDTExsyu+KLnI+3h8bBSm3/ojzhmHNY2tHM9QH89IkZwC87sHdROdX/ebnH5uKTQHw2DWXw6Irqe6oUXSOqquNGNL8XlnVFAsDRkTFYHVs4egqqFZs5ResNtSEARBBCskuDSk8Nsq3a59oLxG8HFThAEfffW9RqsRhhVacgvMxTyiPO0MthSfl3T+xE5m0W5JowFudWSJlii3OZGesGLOaDTg7kHdJK1DDULBloIgCCIYIcGlIQ6dxihazBGiUZqLV6RFcbSAFVBK5vfJ8YiSKuhscdG8Y21YPJsshcSWK1rbP5AthT7o2aBAzREEERiQ4NKQ+iZtUnOeNDS363JdJbjONfT3/D45gi7CaOBMWRoN3mJLDlrbP1RUNUg6jmwp1EPPBgVqjiCIwIGK5rWEvlSK4imgfJnf1+5gBL21WEEHcBfbe64nJzMFe3PHYsOcEVg5YxAWTu6nWGwZ4PvQbLm0OxhsOHhO9Dit1xXK6NmgQM0RBBFYUIRLQ9KTlA2DDhfmZ/fmFFBK5vdJ/WbPCjqhYntXXFOWUmvAPNHS/sGVg+XVsNeLj5ea8eMbKeWkAno2KFBzBEEEHiS4NOTBrDS8su1EMMywVhUDAINI6s0WZ8bcsT15H5dTm8V+s/e8HJfXF6B8ILPUtFuixeRWQ8cn5vxdayO1ListOVa1a4YzejYoUHMEQQQeJLg0JMJogCnSiOY2narndYIB8MjodKz9wXKCqxZr0dT+qogLpd/slQxklloD9p9nx+Dw2RpBIaVFrQ2NFdIWPRsUqDmCIAIPquHSkP3fXg47sQUA87N7YcGkDMW1WHxw1WjJ+WYv9Zx8SK0BM0UakXVzEu4e1M05FNwVrWptWIHIJ2v1qCsLZfQUuCSuCSLwoAiXhhSWXdZ7CbqQlmxBu4OBNcaE3+T0RfXVZiRaTLBZY2SlzVxTbhVVDdhw8JxbTVKKNRqTMm2SzsX1zV5JlEluDRjXa9Kq1sbfXZ+EO0psTULh2gRBcEOCS1PCrXqrg52ldiz94htOISN1c+cSQ57Y65rwTkGFpPN5frOXW/flitIaMED7WhtfBSIhHT0FLolrggg8DAzDhKcKkEh9fT2sVivq6uoQFxfn07kKTldh1jv8o2TCCfZjXko6kU8M8WE0AAzDLW/Zb/Z7c8c6N5t2B4NRy/J5hQ/Xc9RiS/F5zNtYLHrcyhmDVHWkJzNM7SAfLoLQBzX3bzWgCJeGjLg5CfGxUajVaDZhICM1XSaUcuODLbuS+s1ez44uvWpt5DQJkDjzDV8ioMF8bYIg3CHBpSERRgPu4xkQHY6wQub9gnIkdzZzbgZiYoiPiZk2FJZdRu216+KWL20mtVNrZ6lddcEV6LU2FCFRByVdsKFwbYIgrkOCS0PaHQw+O0ruzp64Dn323MyVtq1/UWJ3/nd8TBQeHpmGuWN7cX6zlxo9+rT4PH47Wd26l0CutfGlrk1LKAJHEEQwQLYQGqI0WhNOeFohJFvMPp+z7lor3th5Gnmlds7Hh6UnItFiEj1PdUMrr52EL/gyvshfiHVPAh3pYCHbDC3YUVKJUcvyMXPdfszbWIyZ6/Zj1LJ8GltDEETAQREuDbHXXdN7CT7jOaw5xRqNoT0SsPWYOhucZ22XGvMnxerFIowGTBuUincldDj6yygy0GptpNa17TtdhdF9btBuYS4ESwSOIAgCIMGlKa7jXYKV2aPSMbZvVzdRsPXYBdUEF+BepF51VXz2n9xzctWzjMuwSRJc/jSKDKRaG6nC8mfvHcQjt6djwaQMP6/IHX/4l1FqkiAIf0KCS0MSO/meHpMDu1VYVeyM3HqsEnf27uL8uaXNga8q1E+zAXBufFKYNigVm4svSDonF2zxupg1RLgYRUq97wzgbALRUnSp3VlKzQEEQfgbElwaYovz7xiNztGRuNLUdv16P2wYAPDYB0dUuUZlXZNmXmJslEFKF9//G9pdkuDiExKuxeuANsXrgRxREbvvnqzbU45nxveFKVKbslA1ZwVSapIgCC2gonkNYTcxf3GlqQ2JlijMHpmGDXNGYG/uWORkpiAnMwVv3j8YAbKXi+I600/qvMIRNyf5PCdQy+L1QC/2dr3vUnAwwN8LK/y3IA/U8i8LluYAgiCCHxJcGiJ3E1NCTUMr3i2oQN21FrdoyaRbUrFq5hC/XlsNuKJJUoSQVGEmFkHKyUzB3tyx2DBnBFbOGOQmXNVCq2HVvsLe99ioCEnHn61u9POKrqPWIG5fh50TBEFIhVKKGjMuw4ZYUwQaW9r9cn6hguFJt6RgjXEIfvtpScAW8HeNM2PR1P5eAkdKF59acwL5itfVSAFqOaxaDXIyU/Bd9TW8uv2E6LE9EmM1WFEHavmXqZmaJAiCEIIEl8bsL7vsN7HFIlQwnJOZgmst7Zj/8VG/rkEpr08fhJE9kzkfk9LF5w97hXYHg1X5Z/BeQbmbc72Somo9xwgp5aHb0rDkixMQyqoZDcCDWWmarQlQR2DrNVqJIIjwgwSXxnxwoEKza33xQ2rKU3Cc0zD1Ixc1bCDUtFfYUVKJ5z75mrPLU0lRdTBGVEyRRswZnS44kmrO6HTNCuZd8VVgB/poJYIgQgcSXBqyo6QSX5Rc1Ox6fys8i78VnnWLxOwoqcSKnac1W4NcKqoa/H4NqanBHSWVgt2dSlKAwRpRYS0f1u0pd4t0GQ0dYktrHy5XfBHYgTxaiSCI0MLAMAy13whQX18Pq9WKuro6xMXFKT5Pu4PBqGX5uoz2YbeK1fcPxuJtJwJ+vNAaP7bhS/Vbkvv32jBnhKRNnz2vWERlb+7YgNzkW9oc+HthBc5WN6JHYiwezErTJbKlNuTDRRChh1r7t1pQhEsj9JyjyEZiXthSguoGdQxQ/YU/i8bl+C3J/XtJTQEGe0TFFGnE7NE36b0MN9RoZgi00UoEQYQeJLg0Qu+aHAYIeLEFSCsaV7LByu0OlPv3kpMCVKubklA3MhVIo5UIggg9SHBpRKDV5AQ6fAX/SjdYud2Bcv5eUvyeXGl3MLDGmPCbCX1Q3dCCxE5m2OIooiIXcognCCKYIMGlEUN7JOi9BABAosWEmoYWSeNa1CYuOhKzR6VLKtrnK/hXusHK7Q6UOtrGAHkpQCHBSGJLOsHmZ0YQBBH81a5BwuGzNXovASnWaPxsRA9dxBYATLklBbemJcIWZ+Z1CPeEFVPbj13waQSL3O5AIed6loTYKFlRlGBxmA8GyCGeIIhggwSXRuhdwwUA11rb8caX+llCfHjwO8z6ywE0tTkkiz72uBe2lPi0wSoZBcM3Uig+Jgrzs3vhqxfGSRZbNLNPXYLRz4wgiPBGtuDavXs3pkyZgtTUVBgMBmzevNnt8U8++QTjx49HUlISDAYDiouLvc5ht9vx4IMPwmazwWKxYMiQIfjnP//pdkxaWhoMBoPbv6VLl7odc+zYMYwePRrR0dHo3r07li9f7nWtTZs2oW/fvoiOjsaAAQOwfft2uS9ZFQKhhovLvFMP6n5Yh9Qol5yCf74NVumsRa7ZiocXjsO87N6yUlUUkVGXYPUzIwgifJEtuBoaGjBw4ECsXr2a9/FRo0Zh2bJlvOf42c9+hpMnT+Kzzz7D119/jXvvvRfTp09HUVGR23G/+93vUFlZ6fz3y1/+0vlYfX09xo8fjx49euDw4cN47bXXsGjRIqxdu9Z5zL59+zBz5kzMnj0bRUVFmDZtGqZNm4aSkhK5L9tnhqUnwhYXeB/+BgDxsVE+rc2AjvSaVBiP/1UToQ1WyhBsLtjutbsHdUPWzUmKaoIoIqMuag2vJgiC0ArZRfMTJ07ExIkTeR9/8MEHAQAVFRW8x+zbtw9vvfUWhg0bBgB44YUXsGLFChw+fBiDBw92Hte5c2fYbDbOc3z44YdoaWnBu+++C5PJhP79+6O4uBh//OMf8cgjjwAAVq5ciZycHDz77LMAgMWLFyMvLw+rVq3CmjVrZL1uX4kwGjCqZxL+ceS8ptcVg0FH5OvD2UNg/MEOIdlixjObjuJivXDBOHA9OrTk3gEA4FUQriZCBf9SR7Do5bdEERl1CXY/M4Igwg9darhuu+02fPTRR6iurobD4cDGjRvR1NSEO++80+24pUuXIikpCYMHD8Zrr72GtrY252OFhYW4/fbbYTKZnL+bMGECTp48iZqaGucx2dnZbuecMGECCgsLedfW3NyM+vp6t39qEWvWtilUzlZT1dDsjOKM7JWMRVOFC8ZZXKNDrum3n2X1ULxuT9hoxSt3Z3KuSe4Gq0bESi4UkVEfpRFLgiAIPdDFFuLjjz/Gfffdh6SkJERGRiI2Nhaffvopevbs6TzmV7/6FYYMGYLExETs27cPCxYsQGVlJf74xz8C6KgDS09Pdztv165dnY8lJCTAbrc7f+d6jN1u513bkiVL8PLLL6v1Ut3okRjrl/PyYbNGY8aPb8SKnadEj/WMrPCZcxoNcJul5zkZytU88m+FZ31YvTusNcRbxuA0DKWIjH8gh3iCIIIFXQTXwoULUVtbi507dyI5ORmbN2/G9OnTsWfPHgwY0JGaevrpp53H33LLLTCZTHj00UexZMkSmM1mv61twYIFbteur69H9+7dVTn3g1lpeHX7CWjRiJZoicLCyf0wITMFGw+dE53dxxVZcd3Mdpba8U5BhdfaL9Y3c3pgsREdofSiOdKI5jaH4OvwNDUN5g2WHOb9AznEEwQRDGguuMrKyrBq1SqUlJSgf//+AICBAwdiz549WL16NW9t1fDhw9HW1oaKigr06dMHNpsNFy9edDuG/Zmt++I7hq8uDADMZrPfBJ0p0ohJA1Kw9Zj//ZZqGlrx5PoivPWAcGSFATDy5iS8/PlxzmHEEUYDhqUn4umPizmvw2cyGWE0YOrAFLy9u5x3jS0iYstoAPKfuRMxpgi33wfzBhvMgpEgCIJQjuY1XI2NjR0XNrpfOiIiAg4H/wZcXFwMo9GILl26AACysrKwe/dutLZetwvIy8tDnz59kJCQ4Dzmyy+/dDtPXl4esrKyVHktShiX0VX8IBVw9XYal2HjrHWJMUXAYAD+ceQ8/lZ4Fou3nUDfhV9gyfZSt+OUWBq0Oxh8dlRYWIoF+hwMsP6AemnJQEGPGjKCIAhCX2RHuK5evYozZ844fy4vL0dxcTESExNx4403orq6GufOncOFCxcAACdPngTQEW2y2Wzo27cvevbsiUcffRR/+MMfkJSUhM2bNyMvLw9bt24F0FHsfuDAAYwZMwadO3dGYWEh5s+fjwceeMAppu6//368/PLLmD17NnJzc1FSUoKVK1dixYoVzrXNmzcPd9xxB15//XVMnjwZGzduxFdffeVmHaE1WnahuQohz8hKXulFzkibg4EzKrVgUkfhvBJLAzGRJpWz1Y0+n4MgCIIg9EZ2hOurr77C4MGDnfYNTz/9NAYPHowXX3wRAPDZZ59h8ODBmDx5MgBgxowZGDx4sDNVGBUVhe3bt+OGG27AlClTcMstt+Bvf/sb/vrXv2LSpEkAOtJ6GzduxB133IH+/fvj1Vdfxfz5892EktVqxb/+9S+Ul5dj6NCheOaZZ/Diiy86LSGAjm7I9evXY+3atRg4cCD+8Y9/YPPmzcjMzFR4u3xnWHoiYj1SZP6GFUJsZGViZgq2fy0cfVq3p9yZ8pMqEquuNGNL8XkUll2Gve6ab4v+Aa0bDQiCIAjCHxgYzzYzwo36+npYrVbU1dUhLi7O5/NtP3YBT6wvEj9QRTbMGeFW8/TOnm+xeNsJ0ectnNwPPx+Zjv3fXsaTHx5B7TV+t3eDAXB9JyVaTKhuaPFp3UYD8M3iiW41Zf6m3cFQfRVBEEQIoPb+7Su6dCmGK+0OBi9s0c7lnq8DUWqabvfp/+Ive8slpQY9ZXuNiNgyAIg1RaChpZ33mDmj0zUVWztKKr06CD27JAmCIAhCCTS8WkMOlldLngnoK0LeTlLTdP85VaW4DksobMqu5vXpA/Ho7enwDCAZDcCjt6c7a8i0YEdJJR7/4IjX67XXNeHxD45gR4n/O0sJgiCI0IUiXBqi5Zw8IW8nLf3AgA5PMFeh6bq2nMwUPDO+L/5eWIGKyw0AgEHdE5AaH4N2B6NJOq/dweDlz0s5RSKf7UU4QWlWgiAI3yHBpSFadSiytVd8m6Ip0og5o9MFPbJUXc9P+sMWF827YZsijeiWEONMX/59/zkA2qXz5NheBKv/l1IozUoQBKEOlFLUkGHpiYiPifL7dZI7m0UjEAsmZfCm8+7qe4Oq67HFRQv6TumdzlNiexEO6P13IQiCCCVIcGlIhNGAh0em+f06UiNpCyZl4JvFE7Fwcj/8LKsHFk7uh28WT8QvRt+syjqkDGQWS+cBHem8dpH8Z7uDQWHZZacthdjxrki9X1p6qOmNWn8XgiAIogNKKWpMry6d/HZuobmIfJgijZg9+ia334l1GEqFgfhAZjXSeb6mvdi5j0rmTYYqlGYlCIJQF4pwaUi7g8FvN/vHFkKoK1EO7Q4Gi7eVih8ogfjYKIzL4J9bCfiezlMj7RVh7Jg3CVy/jyxq3ddgg9KsBEEQ6kKCS0NW5Z9GbaN/bCFs1mi89cAQnwuZ1RrJAwC1ja1u8xW58CWdp2baKyczhXPepFr3NdigNCtBEIS6UEpRI9odDN4rqFDlXGyc5ans3khLjlW1Vd9er27EQiwCMiw9Eba4aN7rCqXz1E57ec6bDGcLBEqzEgRBqAsJLo04WF4tOBpHDkIeW2KIeSpVX21WZY0sYhGQvFI7mtq43ebF0nn+SHux8ybDHTbN+vgHR2CAu5FtuKZZCYIgfIEEl0aoWesidfylp7iqaWjG4m0nBIvLEy0m1daZEBslGAFh66/4Xk18bBSW3DvAuTbP15PcySxpHZT2UgabZvVsSPBF8BMEQYQrJLg0Qs1N/2J9Mx7/4IhgbRFX5x4XbHE5ey6bNUa1ddY0tmJV/mmkJVu8omlC9Vcs5kijs+ie6/XY4syIj41CXWMrpb38BKVZCYIg1IEEl0aI1cTIQWzcjFjkSOhc7DrVKpxfsfO0879do2lSivPt9c14v6AcF2qv4R2O+reL9c3O10hpr+uoPYqH0qwEQRC+Q4JLI1xrYtSAryBcSuRI7FzsOtW2tHSNpjW3OSQ9Z/G2E7yPsWLRGhuF6MgIt8L7cE170SgegiCIwIQEl4awNTG//fRrt2HOvuBZG+aLrQN7ruvrLEG1SiaogHs07Q//M1C1c9Y2tuLD2UNgNBrCOu3FF9n0TBsTBEEQ2kM+XBqTk5mC/QuyVStO96wN86U43/VcHeu8C4kWdWc/stE0GDoiL2pJoqqGZsF5jb7iy+ggLaBRPARBEIENRbh0IP+bi3CosPFxzSlUUpzPV1xuijTi9/cMcKZB1dyqq64289oOKMGfnYjBkKajUTwEQRCBDUW4NIZN+6jhycVVEM4WvUuN7/AVl7MRneY2B+bd1Qvxse6RLl8DSF06R/O6u8tByoBsX1BjdJAW0CgegiCIwIYiXBqipKCdjzmj0zmjK0KGlVxwFZfvKKnEos9Ked3fEy1R+OmQH+Eve8oBCdfgIv8bO7JuTvKyHai60ixYKO+KvzsRxdJ0Qp2iWhMMo3jU7p4kCIIIJkhwaYiacwq3HqvEcxP7cW5YfIaVKdZoLJycgQSLiXfT21FSicdEOilrGlrxlz3leOT2dHx2tFLRa1q3pwKDuydg0i2pbrYD7Q4Gf9lbLsk+w9+diMGUpgv0UTzBkJYlCILwJyS4NETNdE5lXRPeLyjHz0em84ouuYaV7Q4Gz33ytei12ejOZ0cr8Z9nx+Dw2RoUnKnCql1nZL2GF7aUYEJmituaxEbKMAD+d2Sa0zPMnxGSYErTBfIoHuqeJAiCoBouTVE7nbN42wmMWpbPW0fERo74Ovc8O+/2nalCbaO02jI2unP4bM0PURP5icXqhlYcLK/2+j1fbZfNGo01DwzBi1P6+6UT0ZNgSNO5InTf9BI1endPBnp3KUEQ4QNFuDREbRd3QDxKwFc3w5XisZgjZF8/r9SOpz8u9tn7y5NAGCkT6Gk6LgLhvrmiZ1qW0pgEQQQSJLg0JMJowMLJ/fDE+iLVzilUvM234UwdmIK1u8u9RERDc7vs67/LMXJHDkLRIb1HygRymk4Ive+bK3qlZSmNSRBEoEEpRY1JsJhVP6drlIBFyM7gbQ6xpQRfdUaKNRpDeyQEdMonENN0wYQeaVm905gEQRBcUIRLY/isFtSAjRJI2XDUwJf9ygBg6sAU3PHaLlVTPv6wHgi0NF0woUdaNpi6SwmCCB9IcGlM9dVmv5276kozthSfR9WVZlXrxDxJsUZjUqYN70hIJ+b0t+FA+WXUuBTjC6U1fUn5+LNmJ5DSdMGEHmnZYOouJQgifCDBpTFqzVD0xGiAZMNQX5if3Rtzx/bEwfJqSYLrodvSsHrWELfo0NAeCbjjtV2qGopSzU7gwucL5y8ftWDrLiUIIjwgwaUxNmuMX86rRTmKAcDGQ+cwd2xPWakiz+hQYdllVVM+weQIH65omZYNxu5SgiBCHyqa15hh6Ymyo1zxsVFIiNVfG7sKITZVBMBrbqNYqkjtlI+cmh1CP8R84dS8jtL3JkEQhL8gwaUxEUYDXp7aX9ZzWtscqGlsU+X6amwxrBDKyUzB6vuHIMFDQIp18Kmd8qGaHcIT6i4lCCLQ0D9sEoZ8+9+rso5vaJHvj8WHzRqNGT++ESt2nlJ8DlYI7SipxOJtpahuaHE+lmiJwsLJ/QQ3NLVTPlSzQ3BB3aUEQQQSFOHSmHYHg/d8NAtVQnxMFOZn98be3LHo1aWTIg8tAzq6/oalJ/L6fNU0tOLJ9UW844YA9VM+rIDjO9p13UR4oVUakyAIQgwSXBpzsLwatdekzSt0JdES5VM6sO5aK97YeQrLd5zAk+uPyC6ydxVCAHw2llQz5UM1O4EHzTAkCIJwh1KKGqOkjsgA4KdDuuEveyq8vIykwnbrrdsj7jKf3a8Ljl+o523hV6vLUM2Uj9bWAwQ/NMOQIAjCGxJcGqOkjogBsG5PBeaMTsPWY3bFpqYMAEaCWis6V4vCBXfh8NkaTiGkZpE6m/JhHeK3HrugWHhRzY7+kB8aQRAENyS4NGZYeiLiY6NQ2yg/rfjO3gqsnDEYyZ3MWPPvM/jP6So/rBC43NCCw2dreKNTahepqxkRIUd4/SA/NIIgCH6ohktj8krtisQW0GFu+ssNRahpaMGR72pUXpk79vom3hocNYvUhYZsP/7BEcHieyKwID80giAIfijCpSHtDgbPffK1z+dZuKUEV5rkW0WwAklKDdjircdR3eA+/5CNOKk1H48iIqEF+aERBEHwQxEuDdlfdllxdMuVyy6+V3L5xeg0Sce5ii3AO+KkRpchRURCC/JDIwiC4IciXBpS+K1/aq6k8lR2b/SxdcKHB75Do0wzVa6Ik69F6hQRCS1ohiFBEAQ/JLg0REqHoFTMkUY0tzlkPafuWgtnBxmLmOUEl92DUJE623nIJ8YCMSIitmaCH7VSzQRBEKEICS4NiY+VN7SaCwMAq8Iux0+LzosKKilIiThJ6TwMlIgIK7J2ltrxafF53to1QhzyQyMIguCGBJeGJHfyTXCxUYO2dmWhshoV6scA8YiTVC+mQIiIcAlDoTUT4pAfGkEQhDdUNK8hNmuMpOMWTu6HN+8fjBSOgvT52b1wtbnNH8uThNEADO2RwPu4WOch4D72R80RP3Lhs6RwReqoIsIdmmFIEAThDkW4NESK6WmKNRo/H5mOCKMBEzJTvKIEW49d0HDF3jgYeJmiutY9VV1plj32R4+IiJAwlLJmgiAIgpADCS4NkWJ6+ttJ/ZxCg6sgXWkBeYo1GgzDwF7frOj5rrjWcIml5KScA9DeIV7MkoIL6pYkCIIglEIpRY1gIypi/GpDEZZs5z9uWHoi4mOiZF//pSkZWDS1v+znccGKPikpObFz6IUS8aT3mgmCIIjghQSXRkiNqDAA3t5djle3HeccrRNhNODhkemyrj0/uzdyMlOQk5mC+dm9lSzficUcgbY2B1raHJJTcp7Y4sy6ezHJEU9yRhURBEEQBBeUUtQIuRGVdXsqsG5PhfNnV3uCuWN74r195ZKsIWxxZswd29P5c1pyrKx1eNLQ3I4H3zuITuZIxcX7M4fdqHsRtZglhSfkH0UQBEH4AkW4NMLXdJTraJ0IowFL7x0g+hwDgEVT+ysyG020CFtY+NIpmZZsUfxctWAtKQDwDuEGOoQuWUIQBEEQvkKCSyPYiIpSPO0JcjJTsOaBIbzn5BMK7Dr4RAabPivIHSsqupQSKLVQfJYUSRYT/ndkGjbMGYG9uWNJbBEEQRA+Y2AYNQfOhB719fWwWq2oq6tDXFycT+faUVKJxz444vOaNswZ4ezoYy0Z7PVNqL7ajESLCTZrjKCtAlvsDnCbjb71wBBYY0yYuW6/z2t1hXWO35s7NqDSczTOhyAIIvRQc/9WA6rh0hC2aH3FzlM+nce1HkyJnYKU8Stbis/7tEZPAnmWntaWFARBEET4QYJLY+aO7YkNB8/65IelRkpOzGxUzjUSLVFu8wfjYztsK1yL+mmWHkEQBBHOkODSmAijAYum9uecNSiG2sOchSI7w9ITYYuLhr1euLvSFmfG7t+MxeGzNW7CDQCl6QiCIAjiB6hoXgfYlJ5nwXtcdCR+MTIdBnh3zmmdkusQhhmixy2a2h+mSKPX3DyapUcQBEEQ16GieRH8WXTHV6zNNS4nRaeU3I6SSjz3yddenl/xsVFYeu8AShESBEEQAUmgFc2T4BJBrz9YIHXOtTsY7P/2MgrLLgNgkHVTMkZQ1IogCIIIYEhwBRmB9gcjCIIgCEKcQNu/qYaLIAiCIAjCz5DgIgiCIAiC8DMkuAiCIAiCIPwMCS6CIAiCIAg/Q4KLIAiCIAjCz5DgIgiCIAiC8DOyBdfu3bsxZcoUpKamwmAwYPPmzW6Pf/LJJxg/fjySkpJgMBhQXFzsdQ673Y4HH3wQNpsNFosFQ4YMwT//+U+3Y6qrqzFr1izExcUhPj4es2fPxtWrV92OOXbsGEaPHo3o6Gh0794dy5cv97rWpk2b0LdvX0RHR2PAgAHYvn273JdMEARBEAThE7IFV0NDAwYOHIjVq1fzPj5q1CgsW7aM9xw/+9nPcPLkSXz22Wf4+uuvce+992L69OkoKipyHjNr1iwcP34ceXl52Lp1K3bv3o1HHnnE+Xh9fT3Gjx+PHj164PDhw3jttdewaNEirF271nnMvn37MHPmTMyePRtFRUWYNm0apk2bhpKSErkvmyAIgiAIQjmMDwBgPv30U87HysvLGQBMUVGR12MWi4X529/+5va7xMREZt26dQzDMExpaSkDgDl06JDz8S+++IIxGAzM+fPnGYZhmDfffJNJSEhgmpubncfk5uYyffr0cf48ffp0ZvLkyW7XGT58OPPoo49Kfo11dXUMAKaurk7ycwiCIAiC0JdA278j9RB5t912Gz766CNMnjwZ8fHx+Pjjj9HU1IQ777wTAFBYWIj4+HjceuutzudkZ2fDaDTiwIEDuOeee1BYWIjbb78dJpPJecyECROwbNky1NTUICEhAYWFhXj66afdrj1hwgSvNKgrzc3NaG5udv5cV1cHoCOiRhAEQRBEcMDu20yADNTRRXB9/PHHuO+++5CUlITIyEjExsbi008/Rc+ePQF01Hh16dLFfaGRkUhMTITdbncek56e7nZM165dnY8lJCTAbrc7f+d6DHsOLpYsWYKXX37Z6/fdu3eX/0IJgiAIgtCVK1euwGq16r0MfQTXwoULUVtbi507dyI5ORmbN2/G9OnTsWfPHgwYMECPJTlZsGCBW1TM4XCgurra2QTgSn19Pbp3747vvvsuIOY0BSN0D9WB7qPv0D30HbqH6kD30XfYe1haWorU1FS9lwNAB8FVVlaGVatWoaSkBP379wcADBw4EHv27MHq1auxZs0a2Gw2XLp0ye15bW1tqK6uhs1mAwDYbDZcvHjR7Rj2Z7Fj2Me5MJvNMJvNbr+Lj48XfE1xcXH0fwofoXuoDnQffYfuoe/QPVQHuo++061bNxiNgeGApfkqGhsbOy7scQMiIiLgcDgAAFlZWaitrcXhw4edj+fn58PhcGD48OHOY3bv3o3W1lbnMXl5eejTpw8SEhKcx3z55Zdu18nLy0NWVpb6L4wgCIIgCIIH2YLr6tWrKC4udvprlZeXo7i4GOfOnQPQ4Z9VXFyM0tJSAMDJkydRXFzsrJvq27cvevbsiUcffRQHDx5EWVkZXn/9deTl5WHatGkAgH79+iEnJwdz5szBwYMHUVBQgLlz52LGjBnO0OD9998Pk8mE2bNn4/jx4/joo4+wcuVKt3TgvHnzsGPHDrz++uv45ptvsGjRInz11VeYO3eu4htGEARBEAQhG7ltjbt27WIAeP176KGHGIZhmPfee4/z8Zdeesl5jlOnTjH33nsv06VLFyY2Npa55ZZbvGwiLl++zMycOZPp1KkTExcXxzz88MPMlStX3I45evQoM2rUKMZsNjPdunVjli5d6rXejz/+mOnduzdjMpmY/v37M9u2bZP7knlpampiXnrpJaapqUm1c4YbdA/Vge6j79A99B26h+pA99F3AvEeGhgmQPolCYIgCIIgQpTAqCQjCIIgCIIIYUhwEQRBEARB+BkSXARBEARBEH6GBBdBEARBEISfCWnBtXv3bkyZMgWpqakwGAxuMxRbW1uRm5uLAQMGwGKxIDU1FT/72c9w4cIFt3NUV1dj1qxZiIuLQ3x8PGbPno2rV6+6HXPs2DGMHj0a0dHR6N69O5YvX+61lk2bNqFv376Ijo7GgAEDsH37drfHGYbBiy++iJSUFMTExCA7OxunT59W72YoxNd7WFFRgdmzZyM9PR0xMTG4+eab8dJLL6GlpcXtOqF8DwF13osszc3NGDRoEAwGg9OehSWU76Na93Dbtm0YPnw4YmJikJCQ4LSjYTl37hwmT56M2NhYdOnSBc8++yza2trcjvn3v/+NIUOGwGw2o2fPnnj//fe9rrN69WqkpaUhOjoaw4cPx8GDB9W4DT6jxn08deoU7r77biQnJyMuLg6jRo3Crl273I4J5fsodA8BYNGiRejbty8sFgsSEhKQnZ2NAwcOuB0T7nsL4Pt9DLr9RccOSb+zfft25vnnn2c++eQTBgDz6aefOh+rra1lsrOzmY8++oj55ptvmMLCQmbYsGHM0KFD3c6Rk5PDDBw4kNm/fz+zZ88epmfPnszMmTOdj9fV1TFdu3ZlZs2axZSUlDAbNmxgYmJimLffftt5TEFBARMREcEsX76cKS0tZV544QUmKiqK+frrr53HLF26lLFarczmzZuZo0ePMlOnTmXS09OZa9eu+e8GScDXe/jFF18wP//5z5n/+7//Y8rKypgtW7YwXbp0YZ555hnnMaF+DxlGnfciy69+9Stm4sSJDACmqKjI+ftQv49q3MN//OMfTEJCAvPWW28xJ0+eZI4fP8589NFHzsfb2tqYzMxMJjs7mykqKmK2b9/OJCcnMwsWLHAe8+233zKxsbHM008/zZSWljJ//vOfmYiICGbHjh3OYzZu3MiYTCbm3XffZY4fP87MmTOHiY+PZy5evOi/GyQRNe5jr169mEmTJjFHjx5lTp06xTzxxBNMbGwsU1lZyTBM6N9HoXvIMAzz4YcfMnl5eUxZWRlTUlLCzJ49m4mLi2MuXbrkPCbc9xaG8f0+Btv+EtKCyxWuP6YnBw8eZAAwZ8+eZRiGYUpLSxkAzKFDh5zHfPHFF4zBYGDOnz/PMAzDvPnmm0xCQgLT3NzsPCY3N5fp06eP8+fp06czkydPdrvW8OHDmUcffZRhGIZxOByMzWZjXnvtNefjtbW1jNlsZjZs2KDsBfsBJfeQi+XLlzPp6enOn8PpHjKMb/dx+/btTN++fZnjx497Ca5wuo9K7mFrayvTrVs35i9/+Qvvc7Zv384YjUbGbrc7f/fWW28xcXFxzvv6m9/8hunfv7/b8+677z5mwoQJzp+HDRvGPPnkk86f29vbmdTUVGbJkiWSX6MWKLmP//3vfxkAzO7du53H1NfXMwCYvLw8hmHC6z5KuYd1dXUMAGbnzp0Mw9DewoWS+8hFIO8vIZ1SlEtdXR0MBoNzdmJhYSHi4+Nx6623Oo/Jzs6G0Wh0hjULCwtx++23w2QyOY+ZMGECTp48iZqaGucx2dnZbteaMGECCgsLAXS49dvtdrdjrFYrhg8f7jwmWPC8h3zHJCYmOn+me+gN1328ePEi5syZg7///e+IjY31eg7dR3c87+GRI0dw/vx5GI1GDB48GCkpKZg4cSJKSkqczyksLMSAAQPQtWtX5+8mTJiA+vp6HD9+3HmM0D1saWnB4cOH3Y4xGo3Izs4OunsIeN/HpKQk9OnTB3/729/Q0NCAtrY2vP322+jSpQuGDh0KgO6jKy0tLVi7di2sVisGDhwIgPYWJXDdRy4CeX8hwfUDTU1NyM3NxcyZM53DQu12O7p06eJ2XGRkJBITE52jiux2u9uHCgDnz2LHuD7u+jyuY4IBrnvoyZkzZ/DnP/8Zjz76qPN3dA/d4bqPDMPg5z//OR577DG3D2lX6D5eh+sefvvttwA66kJeeOEFbN26FQkJCbjzzjtRXV0NwLd7WF9fj2vXrqGqqgrt7e1Bfw8B7vtoMBiwc+dOFBUVoXPnzoiOjsYf//hH7NixwznHlu4jsHXrVnTq1AnR0dFYsWIF8vLykJycDID2FjkI3UdPAn1/IcGFjkLR6dOng2EYvPXWW3ovJyiRcg/Pnz+PnJwc/L//9/8wZ84cjVcYHPDdxz//+c+4cuUKFixYoOPqggO+e+hwOAAAzz//PH76059i6NCheO+992AwGLBp0ya9lhuw8N1HhmHw5JNPokuXLtizZw8OHjyIadOmYcqUKaisrNRxxYHFmDFjUFxcjH379iEnJwfTp0/HpUuX9F5W0CH1PgbD/hL2gov9UDl79izy8vLcIjM2m83rD9vW1obq6mrYbDbnMRcvXnQ7hv1Z7BjXx12fx3VMICN0D1kuXLiAMWPG4LbbbsPatWvdHqN72IHQfczPz0dhYSHMZjMiIyPRs2dPAMCtt96Khx56CADdR0D4HqakpAAAMjIynL8zm8246aabcO7cOQC+3cO4uDjExMQgOTkZERERQXsPAfH34tatW7Fx40aMHDkSQ4YMwZtvvomYmBj89a9/BUD3EQAsFgt69uyJESNG4J133kFkZCTeeecdALS3yEHoPrIEy/4S1oKL/VA5ffo0du7ciaSkJLfHs7KyUFtbi8OHDzt/l5+fD4fDgeHDhzuP2b17N1pbW53H5OXloU+fPs7welZWFr788ku3c+fl5SErKwsAkJ6eDpvN5nZMfX09Dhw44DwmUBG7h0DHN48777zTGVEwGt3fduF+DwHx+/inP/0JR48eRXFxMYqLi50tyx999BFeffVVAHQfxe7h0KFDYTabcfLkSbfnVFRUoEePHgA67s/XX3/tthmygoMVamL30GQyYejQoW7HOBwOfPnllwF/DwHx+9jY2AgAXv8/NhqNzigi3UdvHA4HmpubAdDe4guu9xEIsv1Fcnl9EHLlyhWmqKiIKSoqYgAwf/zjH5mioiLm7NmzTEtLCzN16lTmRz/6EVNcXMxUVlY6/7l2M+Tk5DCDBw9mDhw4wOzdu5fp1auXW+tubW0t07VrV+bBBx9kSkpKmI0bNzKxsbFeLaeRkZHMH/7wB+bEiRPMSy+9xNlyGh8fz2zZsoU5duwYc/fddwdE666v9/D7779nevbsydx1113M999/73YMS6jfQ4ZR573oSnl5uVeXYqjfRzXu4bx585hu3box//d//8d88803zOzZs5kuXbow1dXVDMNctzMYP348U1xczOzYsYO54YYbOO0Mnn32WebEiRPM6tWrOe0MzGYz8/777zOlpaXMI488wsTHx7t17emFr/fxv//9L5OUlMTce++9THFxMXPy5Enm17/+NRMVFcUUFxczDBP691HoHl69epVZsGABU1hYyFRUVDBfffUV8/DDDzNms5kpKSlxniPc9xaG8f0+Btv+EtKCa9euXQwAr38PPfSQc8Pi+rdr1y7nOS5fvszMnDmT6dSpExMXF8c8/PDDzJUrV9yuc/ToUWbUqFGM2WxmunXrxixdutRrLR9//DHTu3dvxmQyMf3792e2bdvm9rjD4WAWLlzIdO3alTGbzcxdd93FnDx50i/3RQ6+3sP33nuP9xhXQvkeMow670VXuAQXw4T2fVTjHra0tDDPPPMM06VLF6Zz585Mdna22ybIMAxTUVHBTJw4kYmJiWGSk5OZZ555hmltbfVay6BBgxiTycTcdNNNzHvvvee13j//+c/MjTfeyJhMJmbYsGHM/v37/XFbZKPGfTx06BAzfvx4JjExkencuTMzYsQIZvv27W7XCeX7KHQPr127xtxzzz1MamoqYzKZmJSUFGbq1KnMwYMH3c4R7nsLw/h+H4NtfzEwDMNIj4cRBEEQBEEQcgnrGi6CIAiCIAgtIMFFEARBEAThZ0hwEQRBEARB+BkSXARBEARBEH6GBBdBEARBEISfIcFFEARBEAThZ0hwEQRBEARB+BkSXARBEARBEH6GBBdBEARBEISfIcFFEARBEAThZ0hwEQRBEARB+BkSXARBEARBEH7m/wPHgBxDndT4DgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%200 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        barredSet = barredJettisonSet  #new 4/10/24 for MN, ban CCB jettisoning from ALL HD's, not just near ones\n",
    "        #barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)  #weaker specification - must be close to be barred\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c4c929d-0208-4785-aa15-5681a493a530",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "9a19b23e-43cc-4609-9ee7-6d1057a7a9e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "amped unit avg and sd usage are 1.00884 0.09867\n",
      "shed unit avg and sd usage are 1.00284 0.093\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1602 out of 8941\n",
      "working on HD 2402 out of 8941\n",
      "working on HD 3203 out of 8941\n",
      "working on HD 4004 out of 8941\n",
      "working on HD 4806 out of 8941\n",
      "working on HD 5606 out of 8941\n",
      "working on HD 6406 out of 8941\n",
      "working on HD 7206 out of 8941\n",
      "working on HD 8006 out of 8941\n",
      "working on HD 8807 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 621\n",
      "overpop contigFail, enclaveFail = 0 0 out of 3083\n",
      "total contigFail, enclaveFail =  0 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "#latestAvg, latestSD = getWeightedAvgAndSD(latestUnitUse,unitPop)\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "#print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ff3288f1-f9ca-473c-886c-ee8d6c2637bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8941 10008\n"
     ]
    }
   ],
   "source": [
    "print(nHDs, nUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ed668f8-bf1e-46eb-963f-cf5e2b935b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see MD code if we need to remove a stuck CCB's from any HDs and then redo the square-up after dropping them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "e7bfa674-8fc8-42c8-86e9-76d334d3c47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"legisl60_1p5ContigGoodPop.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "c0c3a3d8-cf79-40ad-aa2e-b6793266f1d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    }
   ],
   "source": [
    "#SKIP THIS FOR STATES WITH FRAGMENTED VTD'S ###########\n",
    "print(\"prior to patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "    #add more stuff here to convert to vtd lists\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"needsEnclaveRemoval.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7a36c0ba-5a68-445b-a223-b7fa55dfd728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 8941 HD centers\n"
     ]
    }
   ],
   "source": [
    "nHDs = len(vtdGeom)\n",
    "print(\"there are\",nHDs,\"HD centers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "1b794580-5a58-4238-b310-7c87872ed679",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\n",
      "Must have already established unitGeoms, pops, topology -- or read in via next block\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./2024state_HD_output/OH8941opt3ContigGoodPop.csv ./2024state_HD_output/OH8941legislContigGoodPop.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of HDweight should be unity, actually is 0.9999999999998077\n",
      "I will now renormalize\n",
      "normalized weight is now 0.9999999999999998\n",
      "I read in 8941 HD lists of units\n",
      "orig unit avg and sd usage are 1.00275 0.09587\n"
     ]
    },
    {
     "data": {
      "image/png": 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gBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALIsgBAAALKtToBsA0HaKnTUqr6476fZ9ZVV+7AYA2h+CEBCkip01ynx4k2rqGxqtc4TZFBdp91NXANC+EISAIFVeXaea+gYtGpOqvvFRJ62Li7QrOdbhx84AoP0gCAFBrm98lAYnxwS6DQBolzhZGgAAWBZBCAAAWBZBCAAAWJbPQai4uFg33HCDunXrJofDoSFDhuijjz5ybzfGaO7cuerRo4ccDocyMzP1xRdfeOzjxx9/1Lhx4xQdHa3Y2FhNmjRJVVWel/F+8sknOu+88xQREaGePXtq4cKFJ/SyZs0aDRgwQBERERoyZIjWr1/vsd2bXgAAgHX5FITKy8t17rnnKiwsTK+99pp2796thx9+WHFxce6ahQsX6vHHH9fTTz+trVu3KjIyUllZWTpy5Ii7Zty4cfr000+1YcMGrV27Vps3b9ZNN93k3l5ZWalLL71UvXv3VmFhoR588EHdc889evbZZ90177//vsaOHatJkyZpx44duvLKK3XllVdq165dPvUCAAAszPhgxowZZtSoUSfd7nK5TGJionnwwQfd65xOpwkPDzcrVqwwxhize/duI8l8+OGH7prXXnvNhISEmOLiYmOMMU8++aSJi4sztbW1Hq/dv39/9+Nrr73WZGdne7x+Wlqaufnmm73upSkVFRVGkqmoqPCqHmhPdn7jNL1nrDU7v3EGupV2ifcHCF6+fH77dETo3//+t0aMGKHf/e53io+P17Bhw/Tcc8+5t+/fv18lJSXKzMx0r4uJiVFaWpoKCgokSQUFBYqNjdWIESPcNZmZmQoNDdXWrVvdNeeff77s9v+b5C0rK0t79+5VeXm5u+b41zlWc+x1vOnl52pra1VZWemxAAhu+8qqtKu44qRLsbMm0C0CaEM+zSP01Vdf6amnnlJeXp7uvvtuffjhh7r99ttlt9s1YcIElZSUSJISEhI8npeQkODeVlJSovj4eM8mOnVS165dPWpSUlJO2MexbXFxcSopKWnydZrq5ecWLFig+fPne/dmAOjQ4iLtcoTZlLuqqNE6R5hNb03LYNJJIEj5FIRcLpdGjBih+++/X5I0bNgw7dq1S08//bQmTJjQJg3606xZs5SXl+d+XFlZqZ49ewawIwBtJTnWobemZTR5L7bcVUUqr64jCAFByqcg1KNHDw0cONBj3ZlnnqmXXnpJkpSYmChJKi0tVY8ePdw1paWlSk1NddeUlZV57OPo0aP68ccf3c9PTExUaWmpR82xx03VHL+9qV5+Ljw8XOHh4Y28AwCCSXKsg4ADWJxP5wide+652rt3r8e6zz//XL1795YkpaSkKDExUfn5+e7tlZWV2rp1q9LT0yVJ6enpcjqdKiwsdNds3LhRLpdLaWlp7prNmzervr7eXbNhwwb179/ffYVaenq6x+scqzn2Ot70AgAALM6Xs7C3bdtmOnXqZO677z7zxRdfmBdffNF07tzZ/OMf/3DXPPDAAyY2Ntb861//Mp988on57W9/a1JSUkxNTY275rLLLjPDhg0zW7duNVu2bDH9+vUzY8eOdW93Op0mISHBjB8/3uzatcusXLnSdO7c2TzzzDPumvfee8906tTJPPTQQ2bPnj1m3rx5JiwszOzcudOnXhrDVWPoyLgqquV4D4GOyZfPb5+CkDHGvPrqq2bw4MEmPDzcDBgwwDz77LMe210ul5kzZ45JSEgw4eHh5uKLLzZ79+71qPnhhx/M2LFjTVRUlImOjjYTJ040hw8f9qj5+OOPzahRo0x4eLhJTk42DzzwwAm9rF692pxxxhnGbrebQYMGmXXr1vncS2MIQujI+BBvOd5DoGPy5fM7xBhjAntMqv2qrKxUTEyMKioqFB0dHeh2AJ/sKq7QFYu3aO1to7j7fDPxHgIdky+f39xrDAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWBZBCAAAWFanQDcAoHmKnTUqr6476fZ9ZVV+7AYAOiaCENABFTtrlPnwJtXUNzRa5wizKS7S7qeuAKDjIQgBHVB5dZ1q6hu0aEyq+sZHnbQuLtKu5FiHHzsDgI6FIAR0YH3jozQ4OSbQbQBAh8XJ0gAAwLIIQgAAwLIIQgAAwLIIQgAAwLIIQgAAwLK4agwAmtDU5JRMUwB0XAQhADiJuEi7HGE25a4qarTOEWbTW9MyCENAB0QQAoCTSI516K1pGU3eyiR3VZHKq+sIQkAHRBACgEYkxzoIOEAQ42RpAABgWQQhAABgWQQhAABgWQQhAABgWQQhAABgWVw1BgCtgEkXgY6JIAQALcCki0DHRhACgBZg0kWgYyMIAUALMeki0HFxsjQAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALCsFgWhBx54QCEhIcrNzXWvO3LkiHJyctStWzdFRUXpmmuuUWlpqcfzDhw4oOzsbHXu3Fnx8fG66667dPToUY+ad955R2effbbCw8PVt29fLVu27ITXX7Jkifr06aOIiAilpaVp27ZtHtu96QUAAFhXs4PQhx9+qGeeeUZnnXWWx/o777xTr776qtasWaNNmzbp22+/1dVXX+3e3tDQoOzsbNXV1en999/XCy+8oGXLlmnu3Lnumv379ys7O1sXXnihioqKlJubq8mTJ+uNN95w16xatUp5eXmaN2+etm/frqFDhyorK0tlZWVe9wIAACzONMPhw4dNv379zIYNG0xGRoa54447jDHGOJ1OExYWZtasWeOu3bNnj5FkCgoKjDHGrF+/3oSGhpqSkhJ3zVNPPWWio6NNbW2tMcaY6dOnm0GDBnm85pgxY0xWVpb78ciRI01OTo77cUNDg0lKSjILFizwupemVFRUGEmmoqLCq3rAX3Z+4zS9Z6w1O79xBroVeIGfF+Bfvnx+N+uIUE5OjrKzs5WZmemxvrCwUPX19R7rBwwYoF69eqmgoECSVFBQoCFDhighIcFdk5WVpcrKSn366afump/vOysry72Puro6FRYWetSEhoYqMzPTXeNNLz9XW1uryspKjwUAAAQvn+8+v3LlSm3fvl0ffvjhCdtKSkpkt9sVGxvrsT4hIUElJSXumuND0LHtx7Y1VlNZWamamhqVl5eroaHhF2s+++wzr3v5uQULFmj+/PmNjB4AAAQTn44IHTx4UHfccYdefPFFRUREtFVPATNr1ixVVFS4l4MHDwa6JQAA0IZ8CkKFhYUqKyvT2WefrU6dOqlTp07atGmTHn/8cXXq1EkJCQmqq6uT0+n0eF5paakSExMlSYmJiSdcuXXscVM10dHRcjgc6t69u2w22y/WHL+Ppnr5ufDwcEVHR3ssAAAgePkUhC6++GLt3LlTRUVF7mXEiBEaN26c+89hYWHKz893P2fv3r06cOCA0tPTJUnp6enauXOnx9VdGzZsUHR0tAYOHOiuOX4fx2qO7cNut2v48OEeNS6XS/n5+e6a4cOHN9kLAACwNp/OEerSpYsGDx7ssS4yMlLdunVzr580aZLy8vLUtWtXRUdH67bbblN6erp+/etfS5IuvfRSDRw4UOPHj9fChQtVUlKi2bNnKycnR+Hh4ZKkKVOm6IknntD06dN14403auPGjVq9erXWrVvnft28vDxNmDBBI0aM0MiRI7Vo0SJVV1dr4sSJkqSYmJgmewHaq2Jnjcqr6066fV9ZlR+7AYDg5fPJ0k159NFHFRoaqmuuuUa1tbXKysrSk08+6d5us9m0du1a3XLLLUpPT1dkZKQmTJige++9112TkpKidevW6c4779Rjjz2mU089VX/961+VlZXlrhkzZowOHTqkuXPnqqSkRKmpqXr99dc9TqBuqhegPSp21ijz4U2qqW9otM4RZlNcpN1PXQFAcAoxxphAN9FeVVZWKiYmRhUVFZwvBL/ZVVyhKxZv0aIxqeobH3XSurhIu5JjHX7sDM117Ge69rZRGpwcE+h2gKDny+d3qx8RAtA6+sZH8aEJAG2Mm64CAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADLIggBAADL6hToBgAA/1XsrFF5dV2jNXGRdiXHOvzUERD8CEIA0A4UO2uU+fAm1dQ3NFrnCLPprWkZhCGglRCEAKAdKK+uU019gxaNSVXf+KhfrNlXVqXcVUUqr64jCAGthCAEAO1I3/goDU6OCXQbgGVwsjQAALAsjggBgJ/sK6tq1jYAbYcgBABtLC7SLkeYTbmrihqtc4TZFBdp909TACQRhACgzSXHOvTWtAwujQfaIYIQAPhBcqyDkAO0QwQhwM+amjSPc0UAwH8IQoAf+TJpHueKAEDbIwgBfuTNpHkS54oAgL8QhIAAYNI8AGgfmFARAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYVqdANwAA8M2+sqpGt8dF2pUc6/BTN0DHRhACgA4iLtIuR5hNuauKGq1zhNn01rQMwhDgBYIQAHQQybEOvTUtQ+XVdSet2VdWpdxVRSqvriMIAV4gCAFAB5Ic6yDgAK2Ik6UBAIBlcUQIaEXFzpomv7YAALQfBCGglRQ7a5T58CbV1Dc0WucIsyku0u6nrgAAjSEIAa2kvLpONfUNWjQmVX3jo05ax6XNANB+EISAVtY3PkqDk2MC3QYAwAsEIQAIQky6CHiHIAQAQYRJFwHf+HT5/IIFC/SrX/1KXbp0UXx8vK688krt3bvXo+bIkSPKyclRt27dFBUVpWuuuUalpaUeNQcOHFB2drY6d+6s+Ph43XXXXTp69KhHzTvvvKOzzz5b4eHh6tu3r5YtW3ZCP0uWLFGfPn0UERGhtLQ0bdu2zedeACCYHJt0ce1to066LBqTqpr6hkavcASswqcgtGnTJuXk5OiDDz7Qhg0bVF9fr0svvVTV1dXumjvvvFOvvvqq1qxZo02bNunbb7/V1Vdf7d7e0NCg7Oxs1dXV6f3339cLL7ygZcuWae7cue6a/fv3Kzs7WxdeeKGKioqUm5uryZMn64033nDXrFq1Snl5eZo3b562b9+uoUOHKisrS2VlZV73AgDBKDnWocHJMSddGjuZH7Ac0wJlZWVGktm0aZMxxhin02nCwsLMmjVr3DV79uwxkkxBQYExxpj169eb0NBQU1JS4q556qmnTHR0tKmtrTXGGDN9+nQzaNAgj9caM2aMycrKcj8eOXKkycnJcT9uaGgwSUlJZsGCBV730pSKigojyVRUVHhVD2vb+Y3T9J6x1uz8xhnoVoBG8buKYOfL53eLZpauqKiQJHXt2lWSVFhYqPr6emVmZrprBgwYoF69eqmgoECSVFBQoCFDhighIcFdk5WVpcrKSn366afumuP3cazm2D7q6upUWFjoURMaGqrMzEx3jTe9/Fxtba0qKys9FgAAELyaHYRcLpdyc3N17rnnavDgwZKkkpIS2e12xcbGetQmJCSopKTEXXN8CDq2/di2xmoqKytVU1Oj77//Xg0NDb9Yc/w+murl5xYsWKCYmBj30rNnTy/fDQAA0BE1Owjl5ORo165dWrlyZWv2E1CzZs1SRUWFezl48GCgWwIAAG2oWZfPT506VWvXrtXmzZt16qmnutcnJiaqrq5OTqfT40hMaWmpEhMT3TU/v7rr2JVcx9f8/Oqu0tJSRUdHy+FwyGazyWaz/WLN8ftoqpefCw8PV3h4uA/vBAAA6Mh8OiJkjNHUqVP1z3/+Uxs3blRKSorH9uHDhyssLEz5+fnudXv37tWBAweUnp4uSUpPT9fOnTs9ru7asGGDoqOjNXDgQHfN8fs4VnNsH3a7XcOHD/eocblcys/Pd9d40wsAALA2n44I5eTkaPny5frXv/6lLl26uM+1iYmJkcPhUExMjCZNmqS8vDx17dpV0dHRuu2225Senq5f//rXkqRLL71UAwcO1Pjx47Vw4UKVlJRo9uzZysnJcR+NmTJlip544glNnz5dN954ozZu3KjVq1dr3bp17l7y8vI0YcIEjRgxQiNHjtSiRYtUXV2tiRMnuntqqhcAAGBxvlyOJukXl+eff95dU1NTY2699VYTFxdnOnfubK666irz3Xffeezn66+/NpdffrlxOByme/fuZtq0aaa+vt6j5u233zapqanGbreb0047zeM1jlm8eLHp1auXsdvtZuTIkeaDDz7w2O5NL43h8nn4gkuS0VHwu4pg58vnd4gxxgQuhrVvlZWViomJUUVFhaKjowPdDtq5XcUVumLxFq29bRQ3XUW7xu8qgp0vn98tmkcIAACgIyMIAQAAy+Lu84CXip01jd6kcl9ZlR+7AQC0BoIQ4IViZ40yH96kmvqGRuscYTbFRdr91BXQMk2F97hIu5JjHX7qBggMghDghfLqOtXUN2jRmNRG79zNBwc6grhIuxxhNuWuKmq0zhFm01vTMvidRlAjCAE+6BsfxVU26PCSYx16a1pGk1/15q4qUnl1HUEIQY0gBAAWlBzrIOAA4qoxAABgYQQhAABgWQQhAABgWQQhAABgWQQhAABgWQQhAABgWQQhAABgWQQhAABgWQQhAABgWQQhAABgWQQhAABgWQQhAABgWdx0FQBwUvvKqpqsiYu0cwNXdFgEIQDACeIi7XKE2ZS7qqjJWkeYTW9NyyAMoUMiCAEATpAc69Bb0zJUXl3XaN2+sirlripSeXUdQQgdEkEIAPCLkmMdhBsEPU6WBgAAlsURIUBSsbOm0a8AvDlhFADQ8RCEYHnFzhplPrxJNfUNjdY5wmyKi7T7qSsAgD8QhGB55dV1qqlv0KIxqeobH3XSOi4RBk6uqaOm/P1Be0UQAv6/vvFRGpwcE+g2gA7F28vsucQe7RVBCADQbN5cZs8l9mjPCEIAgBbhMnt0ZFw+DwAALIsgBAAALIsgBAAALIsgBAAALIuTpRH0mDUaAHAyBCEENWaNBtoPJl1Ee0QQQlBj1mgg8Jh0Ee0ZQQiWwKzRQOAw6SLaM4IQAKDNMeki2iuuGgMAAJbFESF0aFwRBgBoCYIQOiyuCAMAtBRBCO2WN0d7uCIMANASBCG0S74c7flVSleCDgCgWQhCCAiO9gAA2gOCEPyOoz0AgPaCIAS/Y7ZnAEB7QRBCwDDbMwAg0JhQEQAAWBZBCAAAWBZfjQEAgkpTV6VKnIOI/0MQQqvjthcAmqupfx+aCjC+XJX61rQMwhAIQmhd3PYCQHPERdrlCLMpd1VRo3WOMJueHj9c3U7y74c3c5DtK6tS7qoilVfXEYRAEELr4tJ4AM2RHOvQW9MyGj2a/EN1nab8vVAT/rat0X0xBxl8QRBCm+DSeAC+So51NBlemgpLEv/Rgm8IQgCADsObsOStlp6PhOBAEAIAWIov5yNxQnXwIwgBACzFm/ORjp1Q/eH+H1XO+Y5BjSAEALCcpr5i46iRdRCEAAD4GV+OGnEZfsdGEIKbN7OxNoXJEgEEi9Y8MRvtF0EIkryfCNEbTJYIAOgoCEKQ5P1EiN7g5EEAQEdBELIIb+//xUSIAOAb5iPq2AhCFsD9vwCg9XFlWXCwRBBasmSJHnzwQZWUlGjo0KFavHixRo4cGei2Wo03R3u4/xcAtC6uLAsOQR+EVq1apby8PD399NNKS0vTokWLlJWVpb179yo+Pj7Q7bWYL0d7uAkhALQub68s89fXZ61x9W9r9tMRhBhjTKCbaEtpaWn61a9+pSeeeEKS5HK51LNnT912222aOXNmo8+trKxUTEyMKioqFB0d7Y92fbaruEJXLN7C0R4AaId8+c9qU1+fNRVyfqiu05S/F7ba1b+t8XWeN8GsLT6ffPn8DuojQnV1dSosLNSsWbPc60JDQ5WZmamCgoIT6mtra1VbW+t+XFFRIem/b2hbOFR5RIeqapsubMRXh6rlqv1JiQ6XenUJaaSyXpWV9S16LQCAb7qESv/8wzA5fzp5GPjqULVmvrxTm3b+R6edEvmLNT/+VK/clTt0pN7V6OtFhIXqyeuGqWvnsGb37E0/3vCl539PHaWkVgxDxz63vTrWY4JYcXGxkWTef/99j/V33XWXGTly5An18+bNM5JYWFhYWFhYgmA5ePBgk1khqI8I+WrWrFnKy8tzP3a5XPrxxx/VrVs3hYQ0drSlfamsrFTPnj118ODBdvuVXmtgnMHHKmNlnMGFcbY/xhgdPnxYSUlJTdYGdRDq3r27bDabSktLPdaXlpYqMTHxhPrw8HCFh4d7rIuNjW3LFttUdHR0u/9lbQ2MM/hYZayMM7gwzvYlJibGq7rQNu4joOx2u4YPH678/Hz3OpfLpfz8fKWnpwewMwAA0B4E9REhScrLy9OECRM0YsQIjRw5UosWLVJ1dbUmTpwY6NYAAECABX0QGjNmjA4dOqS5c+eqpKREqampev3115WQkBDo1tpMeHi45s2bd8LXfMGGcQYfq4yVcQYXxtmxBf08QgAAACcT1OcIAQAANIYgBAAALIsgBAAALIsgBAAALIsg1EEtWbJEffr0UUREhNLS0rRt27aT1l5wwQUKCQk5YcnOzvZjx83jyzgladGiRerfv78cDod69uypO++8U0eOHPFTt83nyzjr6+t177336vTTT1dERISGDh2q119/3Y/dNs/mzZs1evRoJSUlKSQkRK+88kqTz3nnnXd09tlnKzw8XH379tWyZcvavM+W8nWc3333na6//nqdccYZCg0NVW5url/6bClfx/nyyy/rkksu0SmnnKLo6Gilp6frjTfe8E+zLeDrOLds2aJzzz1X3bp1k8Ph0IABA/Too4/6p9kWaM7fz2Pee+89derUSampqW3WX1siCHVAq1atUl5enubNm6ft27dr6NChysrKUllZ2S/Wv/zyy/ruu+/cy65du2Sz2fS73/3Oz537xtdxLl++XDNnztS8efO0Z88eLV26VKtWrdLdd9/t58594+s4Z8+erWeeeUaLFy/W7t27NWXKFF111VXasWOHnzv3TXV1tYYOHaolS5Z4Vb9//35lZ2frwgsvVFFRkXJzczV58uR2/+Hp6zhra2t1yimnaPbs2Ro6dGgbd9d6fB3n5s2bdckll2j9+vUqLCzUhRdeqNGjRwfd721kZKSmTp2qzZs3a8+ePZo9e7Zmz56tZ599to07bRlfx3mM0+nU73//e1188cVt1JkftM7tTeFPI0eONDk5Oe7HDQ0NJikpySxYsMCr5z/66KOmS5cupqqqqq1abBW+jjMnJ8dcdNFFHuvy8vLMueee26Z9tpSv4+zRo4d54oknPNZdffXVZty4cW3aZ2uSZP75z382WjN9+nQzaNAgj3VjxowxWVlZbdhZ6/JmnMfLyMgwd9xxR5v101Z8HecxAwcONPPnz2/9htpIc8d51VVXmRtuuKH1G2ojvoxzzJgxZvbs2WbevHlm6NChbdpXW+GIUAdTV1enwsJCZWZmuteFhoYqMzNTBQUFXu1j6dKluu666xQZGdlWbbZYc8Z5zjnnqLCw0P210ldffaX169frN7/5jV96bo7mjLO2tlYREREe6xwOh7Zs2dKmvfpbQUGBx/siSVlZWV7/nqN9c7lcOnz4sLp27RroVtrUjh079P777ysjIyPQrbS6559/Xl999ZXmzZsX6FZaJOhnlg4233//vRoaGk6YGTshIUGfffZZk8/ftm2bdu3apaVLl7ZVi62iOeO8/vrr9f3332vUqFEyxujo0aOaMmVKu/5qrDnjzMrK0iOPPKLzzz9fp59+uvLz8/Xyyy+roaHBHy37TUlJyS++L5WVlaqpqZHD4QhQZ2gNDz30kKqqqnTttdcGupU2ceqpp+rQoUM6evSo7rnnHk2ePDnQLbWqL774QjNnztS7776rTp06dpTgiJDFLF26VEOGDNHIkSMD3Uqre+edd3T//ffrySef1Pbt2/Xyyy9r3bp1+tOf/hTo1lrVY489pn79+mnAgAGy2+2aOnWqJk6cqNBQ/jqjY1i+fLnmz5+v1atXKz4+PtDttIl3331XH330kZ5++mktWrRIK1asCHRLraahoUHXX3+95s+frzPOOCPQ7bRYx45xFtS9e3fZbDaVlpZ6rC8tLVViYmKjz62urtbKlSt17733tmWLraI545wzZ47Gjx/v/p/XkCFDVF1drZtuukl//OMf22VQaM44TznlFL3yyis6cuSIfvjhByUlJWnmzJk67bTT/NGy3yQmJv7i+xIdHc3RoA5s5cqVmjx5stasWXPCV5/BJCUlRdJ//x0qLS3VPffco7Fjxwa4q9Zx+PBhffTRR9qxY4emTp0q6b9fdRpj1KlTJ7355pu66KKLAtyl99rfJwMaZbfbNXz4cOXn57vXuVwu5efnKz09vdHnrlmzRrW1tbrhhhvaus0Wa844f/rppxPCjs1mkySZdnpLvZb8PCMiIpScnKyjR4/qpZde0m9/+9u2btev0tPTPd4XSdqwYUOT7wvarxUrVmjixIlasWJFh5i+o7W4XC7V1tYGuo1WEx0drZ07d6qoqMi9TJkyRf3791dRUZHS0tIC3aJPOCLUAeXl5WnChAkaMWKERo4cqUWLFqm6uloTJ06UJP3+979XcnKyFixY4PG8pUuX6sorr1S3bt0C0bbPfB3n6NGj9cgjj2jYsGFKS0vTvn37NGfOHI0ePdodiNojX8e5detWFRcXKzU1VcXFxbrnnnvkcrk0ffr0QA6jSVVVVdq3b5/78f79+1VUVKSuXbuqV69emjVrloqLi/W///u/kqQpU6boiSee0PTp03XjjTdq48aNWr16tdatWxeoIXjF13FKUlFRkfu5hw4dUlFRkex2uwYOHOjv9r3m6ziXL1+uCRMm6LHHHlNaWppKSkok/fdE/5iYmICMwRu+jnPJkiXq1auXBgwYIOm/0wY89NBDuv322wPSv7d8GWdoaKgGDx7s8fz4+HhFREScsL5DCPBVa2imxYsXm169ehm73W5GjhxpPvjgA/e2jIwMM2HCBI/6zz77zEgyb775pp87bRlfxllfX2/uuecec/rpp5uIiAjTs2dPc+utt5ry8nL/N+4jX8b5zjvvmDPPPNOEh4ebbt26mfHjx5vi4uIAdO2bt99+20g6YTk2tgkTJpiMjIwTnpOammrsdrs57bTTzPPPP+/3vn3VnHH+Un3v3r393rsvfB1nRkZGo/Xtla/jfPzxx82gQYNM586dTXR0tBk2bJh58sknTUNDQ2AG4KXm/N4eryNfPh9iTDv9zgAAAKCNcY4QAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwrP8HivVjiRViZBsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\") #vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology -- or read in via next block\")\n",
    "guessedFile = \"enter the unpatched UNIT list file, e.g. ./2024state_HD_output/\"+STATE+str(nHDs)+\"opt3ContigGoodPop.csv\"\n",
    "infile = input(guessedFile)\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "sumWt = np.sum(HDweight)\n",
    "print(\"sum of HDweight should be unity, actually is\",sumWt)\n",
    "print(\"I will now renormalize\")\n",
    "HDweight = [HDweight[t] /sumWt for t in range(nHDs)]\n",
    "print(\"normalized weight is now\",np.sum(HDweight))\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "#HDunitList = [list() for t in range(nHDs)]\n",
    "#for t in range(nHDs):\n",
    "#    if t%500 == 0:\n",
    "#        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "#    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "#        if v in allUnits:\n",
    "#            HDunitList[t].append(allUnits.index(v))\n",
    "#    for c in unitCounties:\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(c+0.5)\n",
    "#            HDunitList[t].append(u)\n",
    "#    for j, L in enumerate(CCBlist):\n",
    "#        c = L[0]\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(j+0.25)\n",
    "#            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "8014f0bf-74bb-40bc-b217-af4e204ea9a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upon restart, need to pull in unit topology and data\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv ./state_map_files/MNunitTopologies_06Apr24.csv\n"
     ]
    }
   ],
   "source": [
    "#SKIP THE BELOW IF NOT A COLD RESTART *******\n",
    "print(\"upon restart, need to pull in unit topology and data\")  #note, this won't have geometries for plotting, but fine for patching\n",
    "topologyFile = \"enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv\"\n",
    "infile = input(topologyFile)\n",
    "topDF = pd.read_csv(infile)\n",
    "unitCPx, unitCPy, unitPop = topDF[\"centroid x\"], topDF[\"centroid y\"], topDF[\"unitPop\"]\n",
    "nUnits = len(unitPop)\n",
    "unitCP = [Point(unitCPx[u], unitCPy[u]) for u in range(nUnits) ]\n",
    "onBorder = topDF[\"onBorder\"]\n",
    "borderSet = set()\n",
    "for i, onB in enumerate(onBorder):\n",
    "    if onB == 1:\n",
    "        borderSet.add(i)\n",
    "borderList = list(borderSet)\n",
    "\n",
    "nbrListString = topDF[\"neighborList\"]\n",
    "unitNbrs = [ast.literal_eval(nbrListString[u]) for u in range(nUnits)]\n",
    "unitParentVTDno = topDF[\"unitParentVTDno\"]  #NOTE: some units are VTD fragments, so they share a parentVTDno\n",
    "uVLstring = topDF[\"unitVTDlist\"]\n",
    "unitNbrs = [ast.literal_eval(uVLstring[u]) for u in range(nUnits)]\n",
    "allUnits = topDF[\"allUnits\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5f3a5392-11f1-4061-a458-aecd834b2748",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working avg precinct-to-HDcP distance for HD 0\n",
      "working avg precinct-to-HDcP distance for HD 800\n",
      "working avg precinct-to-HDcP distance for HD 1600\n",
      "working avg precinct-to-HDcP distance for HD 2400\n",
      "working avg precinct-to-HDcP distance for HD 3200\n",
      "working avg precinct-to-HDcP distance for HD 4000\n",
      "working avg precinct-to-HDcP distance for HD 4800\n",
      "working avg precinct-to-HDcP distance for HD 5600\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working avg precinct-to-HDcP distance for HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ea0db02f-9df8-4906-acb4-086c29c0fcc1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking for discontiguities for HD 0\n",
      "checking for discontiguities for HD 500\n",
      "checking for discontiguities for HD 1000\n",
      "checking for discontiguities for HD 1500\n",
      "checking for discontiguities for HD 2000\n",
      "checking for discontiguities for HD 2500\n",
      "checking for discontiguities for HD 3000\n",
      "checking for discontiguities for HD 3500\n",
      "checking for discontiguities for HD 4000\n",
      "checking for discontiguities for HD 4500\n",
      "checking for discontiguities for HD 5000\n",
      "checking for discontiguities for HD 5500\n",
      "checking for discontiguities for HD 6000\n",
      "out of 6356 total HDs, there were 6356 0 0 0 HDs that were clean, discontig only, enclave-only, both problems.  Should all be zeroes\n"
     ]
    }
   ],
   "source": [
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()  #optional contiguity check\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"checking for discontiguities for HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems.  Should all be zeroes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "27b42f93-dc87-4afe-8eb3-f30fdff4a737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking unitUse, pop for county clusters\n",
      "9767 0.96194 97574.0\n",
      "9768 0.93466 58240.0\n",
      "9769 0.96076 37102.0\n"
     ]
    }
   ],
   "source": [
    "print(\"checking unitUse, pop for county clusters\")  #not run for legisl OH\n",
    "for uNo in allUnits:\n",
    "    if uNo - int(uNo) > 0.23 and uNo - int(uNo) < 0.27:\n",
    "        u = allUnits.index(uNo)\n",
    "        print(u,r5(unitUse[u]), unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc95ca58-c27f-4ac9-b6f7-234a94945f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "#I believe BELOW UNDERUSED is faster now that we subbed wontEnclave for enclaveCheck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "e9dd457d-fb37-49cf-9b19-19c17c328ede",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.07\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.067\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 2044 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 4046 units out of 10008 with usage below 0.98\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n",
      "Let's further tighten the distro with exchanges up to 5959\n",
      "current avg and SD of unit usage are 1.00284 0.093 . Now trying to increase up to 200 units' underusage\n",
      "all done trying2increase usage of unit 433 final usage = 0.98007 2 sec elapsed 24 0 complete, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00284 0.09256\n",
      "all done trying2increase usage of unit 9258 final usage = 0.85265 11 sec elapsed 18 0 complete, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00284 0.09232\n",
      "all done trying2increase usage of unit 5161 final usage = 0.98361 20 sec elapsed 32 0 complete, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00284 0.09181\n",
      "all done trying2increase usage of unit 7271 final usage = 0.98872 29 sec elapsed 29 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00285 0.09125\n",
      "all done trying2increase usage of unit 6191 final usage = 0.98018 37 sec elapsed 19 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00284 0.0905\n",
      "all done trying2increase usage of unit 5730 final usage = 0.98739 47 sec elapsed 31 0 complete, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00284 0.09015\n",
      "all done trying2increase usage of unit 7293 final usage = 0.9891 55 sec elapsed 27 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00284 0.08979\n",
      "all done trying2increase usage of unit 9139 final usage = 0.85052 64 sec elapsed 15 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00284 0.08964\n",
      "all done trying2increase usage of unit 6195 final usage = 0.98228 73 sec elapsed 21 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00285 0.08894\n",
      "begin usage increase for unit 9055 with usage 0.72999 . Sec, total UU units tried = 78 10\n",
      "all done trying2increase usage of unit 9055 final usage = 0.98315 82 sec elapsed 27 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00285 0.08852\n",
      "all done trying2increase usage of unit 9088 final usage = 0.88894 91 sec elapsed 16 0 complete, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00285 0.08833\n",
      "all done trying2increase usage of unit 5158 final usage = 0.98625 101 sec elapsed 28 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00285 0.08802\n",
      "all done trying2increase usage of unit 7336 final usage = 0.98 110 sec elapsed 20 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00285 0.08767\n",
      "all done trying2increase usage of unit 5342 final usage = 0.98902 119 sec elapsed 25 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00285 0.08736\n",
      "all done trying2increase usage of unit 5463 final usage = 0.98208 127 sec elapsed 24 0 complete, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00284 0.08697\n",
      "all done trying2increase usage of unit 9089 final usage = 0.90561 136 sec elapsed 16 0 complete, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00284 0.0868\n",
      "all done trying2increase usage of unit 6305 final usage = 0.98171 144 sec elapsed 21 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00284 0.08626\n",
      "all done trying2increase usage of unit 7280 final usage = 0.99115 153 sec elapsed 25 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00284 0.08594\n",
      "all done trying2increase usage of unit 5452 final usage = 0.99377 162 sec elapsed 26 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00284 0.08571\n",
      "begin usage increase for unit 9052 with usage 0.74436 . Sec, total UU units tried = 167 20\n",
      "all done trying2increase usage of unit 9052 final usage = 0.98287 170 sec elapsed 24 0 complete, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00284 0.08542\n",
      "all done trying2increase usage of unit 3991 final usage = 0.85179 178 sec elapsed 13 0 complete, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00284 0.08523\n",
      "all done trying2increase usage of unit 6298 final usage = 0.98199 186 sec elapsed 16 0 complete, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00284 0.08496\n",
      "all done trying2increase usage of unit 7283 final usage = 0.98309 195 sec elapsed 25 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00285 0.08469\n",
      "all done trying2increase usage of unit 5173 final usage = 0.98547 204 sec elapsed 26 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00285 0.08448\n",
      "all done trying2increase usage of unit 1945 final usage = 0.99159 212 sec elapsed 19 0 complete, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00285 0.08425\n",
      "all done trying2increase usage of unit 2658 final usage = 0.84097 219 sec elapsed 8 0 complete, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00286 0.08415\n",
      "all done trying2increase usage of unit 7396 final usage = 0.98654 228 sec elapsed 16 0 complete, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00286 0.08366\n",
      "all done trying2increase usage of unit 6304 final usage = 0.9816 237 sec elapsed 18 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00286 0.08344\n",
      "all done trying2increase usage of unit 8542 final usage = 0.9885 246 sec elapsed 24 0 complete, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00286 0.08313\n",
      "begin usage increase for unit 8019 with usage 0.76627 . Sec, total UU units tried = 252 30\n",
      "all done trying2increase usage of unit 8019 final usage = 0.98161 257 sec elapsed 19 0 complete, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00286 0.08274\n",
      "all done trying2increase usage of unit 6484 final usage = 0.98599 267 sec elapsed 22 0 complete, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00286 0.08257\n",
      "all done trying2increase usage of unit 9194 final usage = 0.83012 275 sec elapsed 6 0 complete, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.00286 0.08249\n",
      "all done trying2increase usage of unit 9222 final usage = 0.83853 283 sec elapsed 8 0 complete, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00286 0.08241\n",
      "all done trying2increase usage of unit 6309 final usage = 0.98713 291 sec elapsed 18 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00286 0.08207\n",
      "all done trying2increase usage of unit 5294 final usage = 0.98684 301 sec elapsed 23 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00286 0.08192\n",
      "all done trying2increase usage of unit 2582 final usage = 0.98432 309 sec elapsed 20 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00287 0.08163\n",
      "all done trying2increase usage of unit 2629 final usage = 0.88639 317 sec elapsed 11 0 complete, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00287 0.08148\n",
      "all done trying2increase usage of unit 5746 final usage = 0.98089 326 sec elapsed 21 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00287 0.08119\n",
      "all done trying2increase usage of unit 9193 final usage = 0.87844 334 sec elapsed 10 0 complete, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00288 0.08111\n",
      "begin usage increase for unit 7287 with usage 0.77444 . Sec, total UU units tried = 340 40\n",
      "all done trying2increase usage of unit 7287 final usage = 0.98769 343 sec elapsed 23 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00287 0.08091\n",
      "all done trying2increase usage of unit 3024 final usage = 0.98405 351 sec elapsed 13 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00287 0.08052\n",
      "all done trying2increase usage of unit 5847 final usage = 0.98481 360 sec elapsed 21 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00287 0.08027\n",
      "all done trying2increase usage of unit 961 final usage = 0.9851 368 sec elapsed 18 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00287 0.07991\n",
      "all done trying2increase usage of unit 5295 final usage = 0.98154 379 sec elapsed 24 0 complete, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00287 0.07974\n",
      "all done trying2increase usage of unit 689 final usage = 0.92036 389 sec elapsed 13 0 complete, failed patches, couldn't start= 52\n",
      "current avg and SD of usage are 1.00287 0.07967\n",
      "all done trying2increase usage of unit 688 final usage = 0.92036 398 sec elapsed 13 0 complete, failed patches, couldn't start= 52\n",
      "current avg and SD of usage are 1.00287 0.07962\n",
      "all done trying2increase usage of unit 690 final usage = 0.92036 408 sec elapsed 13 0 complete, failed patches, couldn't start= 52\n",
      "current avg and SD of usage are 1.00287 0.07956\n",
      "all done trying2increase usage of unit 9069 final usage = 0.9925 418 sec elapsed 21 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00287 0.07939\n",
      "all done trying2increase usage of unit 9880 final usage = 0.98257 426 sec elapsed 17 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00287 0.07906\n",
      "begin usage increase for unit 6188 with usage 0.78286 . Sec, total UU units tried = 432 50\n",
      "all done trying2increase usage of unit 6188 final usage = 0.98048 435 sec elapsed 14 0 complete, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00286 0.07882\n",
      "all done trying2increase usage of unit 3992 final usage = 0.96822 444 sec elapsed 18 0 complete, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00287 0.07865\n",
      "all done trying2increase usage of unit 5728 final usage = 0.98422 456 sec elapsed 19 0 complete, failed patches, couldn't start= 68\n",
      "current avg and SD of usage are 1.00287 0.07847\n",
      "all done trying2increase usage of unit 5099 final usage = 0.98113 465 sec elapsed 16 0 complete, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00288 0.07812\n",
      "all done trying2increase usage of unit 7660 final usage = 1.0198 474 sec elapsed 17 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00288 0.07782\n",
      "all done trying2increase usage of unit 2589 final usage = 0.98422 483 sec elapsed 22 0 complete, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00288 0.07758\n",
      "all done trying2increase usage of unit 3998 final usage = 0.91394 491 sec elapsed 13 0 complete, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 1.00288 0.07744\n",
      "all done trying2increase usage of unit 9160 final usage = 0.98774 499 sec elapsed 18 0 complete, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00289 0.0771\n",
      "all done trying2increase usage of unit 5343 final usage = 0.98588 508 sec elapsed 22 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00289 0.077\n",
      "all done trying2increase usage of unit 9730 final usage = 0.98772 516 sec elapsed 18 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.0029 0.07673\n",
      "begin usage increase for unit 5850 with usage 0.78192 . Sec, total UU units tried = 522 60\n",
      "all done trying2increase usage of unit 5850 final usage = 0.98241 525 sec elapsed 22 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.0029 0.07644\n",
      "all done trying2increase usage of unit 7871 final usage = 0.98535 533 sec elapsed 13 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00291 0.07613\n",
      "all done trying2increase usage of unit 545 final usage = 0.92918 542 sec elapsed 15 0 complete, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 1.00291 0.07598\n",
      "all done trying2increase usage of unit 2578 final usage = 0.98353 550 sec elapsed 17 0 complete, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00291 0.0758\n",
      "all done trying2increase usage of unit 5059 final usage = 0.98291 559 sec elapsed 16 0 complete, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00291 0.07539\n",
      "all done trying2increase usage of unit 106 final usage = 0.98283 566 sec elapsed 14 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00291 0.07516\n",
      "all done trying2increase usage of unit 9259 final usage = 0.97087 575 sec elapsed 21 0 complete, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00291 0.075\n",
      "all done trying2increase usage of unit 5734 final usage = 0.99005 586 sec elapsed 23 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00291 0.07487\n",
      "all done trying2increase usage of unit 2660 final usage = 0.87182 593 sec elapsed 10 0 complete, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00291 0.0748\n",
      "all done trying2increase usage of unit 9171 final usage = 0.98206 601 sec elapsed 17 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00291 0.07454\n",
      "begin usage increase for unit 9054 with usage 0.79665 . Sec, total UU units tried = 606 70\n",
      "all done trying2increase usage of unit 9054 final usage = 0.99654 609 sec elapsed 19 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00292 0.07435\n",
      "all done trying2increase usage of unit 9042 final usage = 0.98229 618 sec elapsed 18 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00292 0.07421\n",
      "all done trying2increase usage of unit 950 final usage = 0.98343 626 sec elapsed 17 0 complete, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00292 0.07398\n",
      "all done trying2increase usage of unit 8022 final usage = 0.99147 635 sec elapsed 18 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00292 0.07371\n",
      "all done trying2increase usage of unit 5076 final usage = 0.98763 643 sec elapsed 13 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00291 0.07335\n",
      "all done trying2increase usage of unit 7277 final usage = 0.98257 651 sec elapsed 20 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00292 0.07317\n",
      "all done trying2increase usage of unit 5084 final usage = 0.98215 659 sec elapsed 14 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00291 0.07287\n",
      "all done trying2increase usage of unit 2587 final usage = 0.98737 668 sec elapsed 21 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00291 0.07274\n",
      "all done trying2increase usage of unit 5091 final usage = 0.98337 676 sec elapsed 15 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00291 0.07242\n",
      "all done trying2increase usage of unit 5729 final usage = 0.98485 686 sec elapsed 21 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00292 0.07227\n",
      "begin usage increase for unit 5994 with usage 0.80298 . Sec, total UU units tried = 691 80\n",
      "all done trying2increase usage of unit 5994 final usage = 0.98069 694 sec elapsed 17 0 complete, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00292 0.07206\n",
      "all done trying2increase usage of unit 5848 final usage = 0.978 703 sec elapsed 16 0 complete, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 1.00293 0.07192\n",
      "all done trying2increase usage of unit 5363 final usage = 0.99605 710 sec elapsed 20 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00293 0.0717\n",
      "all done trying2increase usage of unit 6312 final usage = 0.98217 718 sec elapsed 11 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00292 0.07164\n",
      "all done trying2increase usage of unit 456 final usage = 0.99076 727 sec elapsed 14 0 complete, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00292 0.07152\n",
      "all done trying2increase usage of unit 2663 final usage = 0.8612 735 sec elapsed 7 0 complete, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.00292 0.07148\n",
      "all done trying2increase usage of unit 7881 final usage = 0.98036 744 sec elapsed 15 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00292 0.07126\n",
      "all done trying2increase usage of unit 7250 final usage = 0.99152 753 sec elapsed 19 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00293 0.07114\n",
      "all done trying2increase usage of unit 5283 final usage = 0.98906 763 sec elapsed 20 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00292 0.07105\n",
      "all done trying2increase usage of unit 3996 final usage = 0.98866 771 sec elapsed 21 0 complete, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00292 0.07082\n",
      "begin usage increase for unit 94 with usage 0.81026 . Sec, total UU units tried = 777 90\n",
      "all done trying2increase usage of unit 94 final usage = 0.98237 780 sec elapsed 14 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00293 0.07067\n",
      "all done trying2increase usage of unit 9204 final usage = 0.93616 789 sec elapsed 13 0 complete, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00293 0.07051\n",
      "all done trying2increase usage of unit 9914 final usage = 0.9862 798 sec elapsed 19 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00293 0.07035\n",
      "all done trying2increase usage of unit 5731 final usage = 0.98318 809 sec elapsed 19 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00293 0.07025\n",
      "all done trying2increase usage of unit 5820 final usage = 0.98805 818 sec elapsed 19 0 complete, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00294 0.07016\n",
      "all done trying2increase usage of unit 933 final usage = 0.98156 827 sec elapsed 18 0 complete, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00295 0.07\n",
      "all done trying2increase usage of unit 1184 final usage = 0.9827 834 sec elapsed 15 0 complete, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00295 0.06978\n",
      "all done trying2increase usage of unit 8557 final usage = 0.98801 844 sec elapsed 19 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00295 0.06962\n",
      "all done trying2increase usage of unit 1289 final usage = 0.9875 852 sec elapsed 13 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00295 0.06944\n",
      "all done trying2increase usage of unit 7272 final usage = 0.99146 862 sec elapsed 17 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00295 0.06932\n",
      "begin usage increase for unit 9680 with usage 0.81505 . Sec, total UU units tried = 867 100\n",
      "all done trying2increase usage of unit 9680 final usage = 0.9909 871 sec elapsed 16 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00295 0.06911\n",
      "all done trying2increase usage of unit 4844 final usage = 0.98196 878 sec elapsed 13 0 complete, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00295 0.06896\n",
      "all done trying2increase usage of unit 2647 final usage = 0.90982 887 sec elapsed 11 0 complete, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00295 0.06892\n",
      "all done trying2increase usage of unit 2695 final usage = 0.89004 894 sec elapsed 11 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00295 0.06884\n",
      "all done trying2increase usage of unit 6306 final usage = 0.98909 902 sec elapsed 9 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00295 0.06871\n",
      "all done trying2increase usage of unit 9044 final usage = 0.98172 913 sec elapsed 15 0 complete, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00295 0.06863\n",
      "all done trying2increase usage of unit 3652 final usage = 0.98519 922 sec elapsed 14 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00295 0.0684\n",
      "all done trying2increase usage of unit 5864 final usage = 0.98112 931 sec elapsed 16 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00295 0.06827\n",
      "all done trying2increase usage of unit 8891 final usage = 0.98544 939 sec elapsed 13 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00295 0.06805\n",
      "all done trying2increase usage of unit 1157 final usage = 0.98027 948 sec elapsed 13 0 complete, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00295 0.0678\n",
      "begin usage increase for unit 2398 with usage 0.81946 . Sec, total UU units tried = 954 110\n",
      "all done trying2increase usage of unit 2398 final usage = 0.98798 956 sec elapsed 17 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00295 0.06747\n",
      "all done trying2increase usage of unit 2568 final usage = 0.98378 967 sec elapsed 15 0 complete, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00295 0.06741\n",
      "all done trying2increase usage of unit 5736 final usage = 0.99088 978 sec elapsed 19 0 complete, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00295 0.06735\n",
      "all done trying2increase usage of unit 4481 final usage = 0.98152 986 sec elapsed 17 0 complete, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00295 0.06719\n",
      "all done trying2increase usage of unit 9624 final usage = 0.98961 995 sec elapsed 15 0 complete, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00295 0.06699\n"
     ]
    }
   ],
   "source": [
    "#FIRST PATCHING BLOCK - boosting underuse.  Suppresses long strings.  For some states (e.g. MA), do overused first. MA:over-und-over-und\n",
    "#  When running Ohio the first time, 20 units took 7800 seconds\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.07  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "#print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = 0.98 #float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.98\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = 0.05 #float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = 10 #int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "        idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying2increase usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"complete, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "861f29d3-b727-49af-90a8-aa9a963e0741",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00284 1.00295 and their SDs are 0.093 0.06699\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1600 out of 8941\n",
      "working on HD 2400 out of 8941\n",
      "working on HD 3200 out of 8941\n",
      "working on HD 4000 out of 8941\n",
      "working on HD 4800 out of 8941\n",
      "working on HD 5600 out of 8941\n",
      "working on HD 6400 out of 8941\n",
      "working on HD 7200 out of 8941\n",
      "working on HD 8000 out of 8941\n",
      "working on HD 8800 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "1ee72cf0-618e-411f-aa37-518ac2c679ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "#optional intermediate save - skip for OH legisl\n",
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"LegislunderPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "77c07848-50c2-4fe9-8841-768b02bf4ac4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.03\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 3442 units out of 10008 with usage above 1.03\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n",
      "this block reduces overuse, with exchanges up to 5959\n",
      "current avg and SD of unit usage are 1.00295 0.06699 . Now trying to reduce up to 3442 units' overusage\n",
      "starting to reduce usage for unit 522 with usage 1.30265 . Total units tried = 1\n",
      "try to drop unit 522 from 9 th HD.  usage down to 1.2543131678702641\n",
      "try to drop unit 522 from 18 th HD.  usage down to 1.2176562835821154\n",
      "try to drop unit 522 from 27 th HD.  usage down to 1.1895490365312331\n",
      "try to drop unit 522 from 36 th HD.  usage down to 1.1747822440507738\n",
      "try to drop unit 522 from 45 th HD.  usage down to 1.1274110449912846\n",
      "all done trying to reduce usage of unit 522 final usage = 1.11681 2 sec elapsed 16 0 successful, failed patches, couldn't start= 33\n",
      "current avg and SD of usage are 1.00295 0.06665\n",
      "starting to reduce usage for unit 6986 with usage 1.28973 . Total units tried = 2\n",
      "try to drop unit 6986 from 12 th HD.  usage down to 1.2112881043248551\n",
      "try to drop unit 6986 from 24 th HD.  usage down to 1.119482284255976\n",
      "all done trying to reduce usage of unit 6986 final usage = 1.02206 8 sec elapsed 26 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00293 0.06621\n",
      "starting to reduce usage for unit 2427 with usage 1.29903 . Total units tried = 3\n",
      "try to drop unit 2427 from 11 th HD.  usage down to 1.1997263770306024\n",
      "try to drop unit 2427 from 22 th HD.  usage down to 1.130599329731438\n",
      "try to drop unit 2427 from 33 th HD.  usage down to 1.0539043012860283\n",
      "all done trying to reduce usage of unit 2427 final usage = 1.02948 14 sec elapsed 28 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00291 0.0657\n",
      "starting to reduce usage for unit 5939 with usage 1.23444 . Total units tried = 4\n",
      "try to drop unit 5939 from 10 th HD.  usage down to 1.1453409515427657\n",
      "all done trying to reduce usage of unit 5939 final usage = 1.02442 20 sec elapsed 19 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0029 0.0654\n",
      "starting to reduce usage for unit 499 with usage 1.23091 . Total units tried = 5\n",
      "try to drop unit 499 from 8 th HD.  usage down to 1.1673065553575461\n",
      "try to drop unit 499 from 16 th HD.  usage down to 1.0621686709412554\n",
      "all done trying to reduce usage of unit 499 final usage = 1.02976 26 sec elapsed 13 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00289 0.06506\n",
      "starting to reduce usage for unit 7075 with usage 1.25517 . Total units tried = 6\n",
      "try to drop unit 7075 from 10 th HD.  usage down to 1.1417499360986219\n",
      "all done trying to reduce usage of unit 7075 final usage = 1.02731 33 sec elapsed 19 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00288 0.06473\n",
      "starting to reduce usage for unit 2538 with usage 1.21899 . Total units tried = 7\n",
      "try to drop unit 2538 from 11 th HD.  usage down to 1.1792206720179204\n",
      "try to drop unit 2538 from 22 th HD.  usage down to 1.0746952738806437\n",
      "all done trying to reduce usage of unit 2538 final usage = 1.01992 39 sec elapsed 17 7 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00287 0.06437\n",
      "starting to reduce usage for unit 7326 with usage 1.21889 . Total units tried = 8\n",
      "try to drop unit 7326 from 12 th HD.  usage down to 1.2021259808086722\n",
      "try to drop unit 7326 from 24 th HD.  usage down to 1.1024920826805422\n",
      "try to drop unit 7326 from 36 th HD.  usage down to 1.033004255792368\n",
      "all done trying to reduce usage of unit 7326 final usage = 1.02429 46 sec elapsed 20 3 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00286 0.06421\n",
      "starting to reduce usage for unit 3154 with usage 1.21156 . Total units tried = 9\n",
      "try to drop unit 3154 from 12 th HD.  usage down to 1.2115565914609783\n",
      "try to drop unit 3154 from 24 th HD.  usage down to 1.2115565914609783\n",
      "try to drop unit 3154 from 36 th HD.  usage down to 1.2115565914609783\n",
      "try to drop unit 3154 from 48 th HD.  usage down to 1.2115565914609783\n",
      "try to drop unit 3154 from 60 th HD.  usage down to 1.2115565914609783\n",
      "all done trying to reduce usage of unit 3154 final usage = 1.21156 52 sec elapsed 0 0 successful, failed patches, couldn't start= 62\n",
      "current avg and SD of usage are 1.00286 0.06421\n",
      "starting to reduce usage for unit 2284 with usage 1.21242 . Total units tried = 10\n",
      "try to drop unit 2284 from 8 th HD.  usage down to 1.1468595819058856\n",
      "try to drop unit 2284 from 16 th HD.  usage down to 1.070013529446493\n",
      "all done trying to reduce usage of unit 2284 final usage = 1.02207 58 sec elapsed 20 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00285 0.06392\n",
      "starting to reduce usage for unit 3156 with usage 1.21156 . Total units tried = 11\n",
      "try to drop unit 3156 from 8 th HD.  usage down to 1.132487129906507\n",
      "try to drop unit 3156 from 16 th HD.  usage down to 1.0484338758898721\n",
      "all done trying to reduce usage of unit 3156 final usage = 1.01736 65 sec elapsed 18 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00285 0.06371\n",
      "starting to reduce usage for unit 1823 with usage 1.2086 . Total units tried = 12\n",
      "try to drop unit 1823 from 7 th HD.  usage down to 1.0975502413333673\n",
      "all done trying to reduce usage of unit 1823 final usage = 1.02867 71 sec elapsed 10 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00284 0.0635\n",
      "starting to reduce usage for unit 2902 with usage 1.20305 . Total units tried = 13\n",
      "try to drop unit 2902 from 7 th HD.  usage down to 1.1209421830580308\n",
      "try to drop unit 2902 from 14 th HD.  usage down to 1.072455084339511\n",
      "all done trying to reduce usage of unit 2902 final usage = 1.0281 77 sec elapsed 13 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00284 0.06328\n",
      "starting to reduce usage for unit 8941 with usage 1.20197 . Total units tried = 14\n",
      "try to drop unit 8941 from 10 th HD.  usage down to 1.1198598442910912\n",
      "try to drop unit 8941 from 20 th HD.  usage down to 1.0466299779447976\n",
      "all done trying to reduce usage of unit 8941 final usage = 1.01913 83 sec elapsed 14 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00282 0.06301\n",
      "starting to reduce usage for unit 432 with usage 1.19729 . Total units tried = 15\n",
      "try to drop unit 432 from 8 th HD.  usage down to 1.1181482387988475\n",
      "try to drop unit 432 from 16 th HD.  usage down to 1.039422776387391\n",
      "all done trying to reduce usage of unit 432 final usage = 1.01947 89 sec elapsed 13 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00281 0.06273\n",
      "starting to reduce usage for unit 7183 with usage 1.18831 . Total units tried = 16\n",
      "try to drop unit 7183 from 9 th HD.  usage down to 1.188307283527247\n",
      "try to drop unit 7183 from 18 th HD.  usage down to 1.188307283527247\n",
      "try to drop unit 7183 from 27 th HD.  usage down to 1.188307283527247\n",
      "try to drop unit 7183 from 36 th HD.  usage down to 1.188307283527247\n",
      "try to drop unit 7183 from 45 th HD.  usage down to 1.188307283527247\n",
      "all done trying to reduce usage of unit 7183 final usage = 1.18831 95 sec elapsed 0 0 successful, failed patches, couldn't start= 49\n",
      "current avg and SD of usage are 1.00281 0.06273\n",
      "starting to reduce usage for unit 5623 with usage 1.18854 . Total units tried = 17\n",
      "try to drop unit 5623 from 8 th HD.  usage down to 1.1223433502991425\n",
      "try to drop unit 5623 from 16 th HD.  usage down to 1.057025464242097\n",
      "all done trying to reduce usage of unit 5623 final usage = 1.01966 102 sec elapsed 17 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.0028 0.06251\n",
      "starting to reduce usage for unit 287 with usage 1.18831 . Total units tried = 18\n",
      "try to drop unit 287 from 6 th HD.  usage down to 1.1159332199269811\n",
      "try to drop unit 287 from 12 th HD.  usage down to 1.0631335465863219\n",
      "all done trying to reduce usage of unit 287 final usage = 1.02393 109 sec elapsed 14 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00279 0.0623\n",
      "starting to reduce usage for unit 6631 with usage 1.18748 . Total units tried = 19\n",
      "try to drop unit 6631 from 8 th HD.  usage down to 1.1405333537636877\n",
      "try to drop unit 6631 from 16 th HD.  usage down to 1.1038345183605334\n",
      "try to drop unit 6631 from 24 th HD.  usage down to 1.0608681863761888\n",
      "all done trying to reduce usage of unit 6631 final usage = 1.01971 115 sec elapsed 15 0 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00278 0.0621\n",
      "starting to reduce usage for unit 9998 with usage 1.18346 . Total units tried = 20\n",
      "try to drop unit 9998 from 6 th HD.  usage down to 1.1289296753543563\n",
      "try to drop unit 9998 from 12 th HD.  usage down to 1.066607098908414\n",
      "all done trying to reduce usage of unit 9998 final usage = 1.02508 122 sec elapsed 16 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00277 0.0619\n",
      "starting to reduce usage for unit 1214 with usage 1.18237 . Total units tried = 21\n",
      "try to drop unit 1214 from 8 th HD.  usage down to 1.1215546693370095\n",
      "all done trying to reduce usage of unit 1214 final usage = 1.0277 128 sec elapsed 12 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00276 0.06157\n",
      "starting to reduce usage for unit 6240 with usage 1.23181 . Total units tried = 22\n",
      "try to drop unit 6240 from 9 th HD.  usage down to 1.130532207947306\n",
      "try to drop unit 6240 from 18 th HD.  usage down to 1.0673370483093314\n",
      "all done trying to reduce usage of unit 6240 final usage = 1.01975 136 sec elapsed 18 4 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00275 0.06126\n",
      "starting to reduce usage for unit 496 with usage 1.18127 . Total units tried = 23\n",
      "try to drop unit 496 from 9 th HD.  usage down to 1.150517719133997\n",
      "try to drop unit 496 from 18 th HD.  usage down to 1.1042456392875577\n",
      "try to drop unit 496 from 27 th HD.  usage down to 1.0895627490370947\n",
      "try to drop unit 496 from 36 th HD.  usage down to 1.0704330405964988\n",
      "all done trying to reduce usage of unit 496 final usage = 1.02905 143 sec elapsed 13 0 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 1.00274 0.06102\n",
      "starting to reduce usage for unit 7050 with usage 1.18029 . Total units tried = 24\n",
      "try to drop unit 7050 from 9 th HD.  usage down to 1.1423288614857465\n",
      "try to drop unit 7050 from 18 th HD.  usage down to 1.1124428871588188\n",
      "try to drop unit 7050 from 27 th HD.  usage down to 1.0499357258068522\n",
      "all done trying to reduce usage of unit 7050 final usage = 1.02624 149 sec elapsed 15 0 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00273 0.06084\n",
      "starting to reduce usage for unit 5322 with usage 1.22779 . Total units tried = 25\n",
      "try to drop unit 5322 from 11 th HD.  usage down to 1.1136762499398605\n",
      "all done trying to reduce usage of unit 5322 final usage = 1.02522 155 sec elapsed 18 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00271 0.0606\n",
      "starting to reduce usage for unit 2988 with usage 1.1778 . Total units tried = 26\n",
      "try to drop unit 2988 from 9 th HD.  usage down to 1.076096441121598\n",
      "all done trying to reduce usage of unit 2988 final usage = 1.02659 161 sec elapsed 12 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0027 0.06032\n",
      "starting to reduce usage for unit 2280 with usage 1.17744 . Total units tried = 27\n",
      "try to drop unit 2280 from 10 th HD.  usage down to 1.0919371821461819\n",
      "all done trying to reduce usage of unit 2280 final usage = 1.02844 167 sec elapsed 14 2 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00269 0.06006\n",
      "starting to reduce usage for unit 7921 with usage 1.18201 . Total units tried = 28\n",
      "try to drop unit 7921 from 9 th HD.  usage down to 1.0847887121499034\n",
      "all done trying to reduce usage of unit 7921 final usage = 1.02497 173 sec elapsed 13 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00268 0.05988\n",
      "starting to reduce usage for unit 5230 with usage 1.17693 . Total units tried = 29\n",
      "try to drop unit 5230 from 6 th HD.  usage down to 1.120765988375048\n",
      "try to drop unit 5230 from 12 th HD.  usage down to 1.052092013117894\n",
      "all done trying to reduce usage of unit 5230 final usage = 1.01916 179 sec elapsed 13 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00267 0.05971\n",
      "starting to reduce usage for unit 8608 with usage 1.1748 . Total units tried = 30\n",
      "try to drop unit 8608 from 9 th HD.  usage down to 1.0761551726826888\n",
      "all done trying to reduce usage of unit 8608 final usage = 1.02879 185 sec elapsed 12 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00266 0.05948\n",
      "starting to reduce usage for unit 2899 with usage 1.17731 . Total units tried = 31\n",
      "try to drop unit 2899 from 8 th HD.  usage down to 1.09178615813211\n",
      "all done trying to reduce usage of unit 2899 final usage = 1.02867 191 sec elapsed 11 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00266 0.05932\n",
      "starting to reduce usage for unit 6607 with usage 1.16984 . Total units tried = 32\n",
      "try to drop unit 6607 from 9 th HD.  usage down to 1.078537996014671\n",
      "all done trying to reduce usage of unit 6607 final usage = 1.02005 197 sec elapsed 13 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00265 0.05915\n",
      "starting to reduce usage for unit 5195 with usage 1.16898 . Total units tried = 33\n",
      "try to drop unit 5195 from 10 th HD.  usage down to 1.096736389702261\n",
      "all done trying to reduce usage of unit 5195 final usage = 1.02345 204 sec elapsed 14 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00264 0.05902\n",
      "starting to reduce usage for unit 8400 with usage 1.17791 . Total units tried = 34\n",
      "try to drop unit 8400 from 8 th HD.  usage down to 1.0665651477932667\n",
      "all done trying to reduce usage of unit 8400 final usage = 1.02803 210 sec elapsed 10 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00264 0.05879\n",
      "starting to reduce usage for unit 2426 with usage 1.17216 . Total units tried = 35\n",
      "try to drop unit 2426 from 11 th HD.  usage down to 1.0737891297966344\n",
      "all done trying to reduce usage of unit 2426 final usage = 1.02319 216 sec elapsed 16 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00263 0.05864\n",
      "starting to reduce usage for unit 1323 with usage 1.16619 . Total units tried = 36\n",
      "try to drop unit 1323 from 8 th HD.  usage down to 1.1661906556983315\n",
      "try to drop unit 1323 from 16 th HD.  usage down to 1.1661906556983315\n",
      "try to drop unit 1323 from 24 th HD.  usage down to 1.1661906556983315\n",
      "try to drop unit 1323 from 32 th HD.  usage down to 1.137043020995418\n",
      "try to drop unit 1323 from 40 th HD.  usage down to 1.1185677499488433\n",
      "all done trying to reduce usage of unit 1323 final usage = 1.10524 222 sec elapsed 4 0 successful, failed patches, couldn't start= 39\n",
      "current avg and SD of usage are 1.00263 0.05856\n",
      "starting to reduce usage for unit 8757 with usage 1.16544 . Total units tried = 37\n",
      "try to drop unit 8757 from 8 th HD.  usage down to 1.09957228507627\n",
      "all done trying to reduce usage of unit 8757 final usage = 1.0255 229 sec elapsed 13 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00262 0.05834\n",
      "starting to reduce usage for unit 6499 with usage 1.16492 . Total units tried = 38\n",
      "try to drop unit 6499 from 7 th HD.  usage down to 1.1068046573026356\n",
      "try to drop unit 6499 from 14 th HD.  usage down to 1.0344893252633711\n",
      "all done trying to reduce usage of unit 6499 final usage = 1.02308 236 sec elapsed 13 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00261 0.05809\n",
      "starting to reduce usage for unit 1685 with usage 1.16276 . Total units tried = 39\n",
      "try to drop unit 1685 from 8 th HD.  usage down to 1.0718258176145525\n",
      "all done trying to reduce usage of unit 1685 final usage = 1.02371 242 sec elapsed 11 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00261 0.05797\n",
      "starting to reduce usage for unit 1175 with usage 1.1625 . Total units tried = 40\n",
      "try to drop unit 1175 from 9 th HD.  usage down to 1.0857200269028793\n",
      "all done trying to reduce usage of unit 1175 final usage = 1.01978 248 sec elapsed 13 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.0026 0.05779\n",
      "starting to reduce usage for unit 695 with usage 1.16227 . Total units tried = 41\n",
      "try to drop unit 695 from 10 th HD.  usage down to 1.0836476418217929\n",
      "all done trying to reduce usage of unit 695 final usage = 1.02731 255 sec elapsed 13 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00259 0.05763\n",
      "starting to reduce usage for unit 1182 with usage 1.1629 . Total units tried = 42\n",
      "try to drop unit 1182 from 8 th HD.  usage down to 1.0733024968624874\n",
      "all done trying to reduce usage of unit 1182 final usage = 1.02199 261 sec elapsed 11 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00258 0.05748\n",
      "starting to reduce usage for unit 2389 with usage 1.18235 . Total units tried = 43\n",
      "try to drop unit 2389 from 8 th HD.  usage down to 1.1136594694938333\n",
      "try to drop unit 2389 from 16 th HD.  usage down to 1.036998001940434\n",
      "all done trying to reduce usage of unit 2389 final usage = 1.02957 267 sec elapsed 16 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00257 0.05731\n",
      "starting to reduce usage for unit 9166 with usage 1.16064 . Total units tried = 44\n",
      "try to drop unit 9166 from 8 th HD.  usage down to 1.0713475749033845\n",
      "all done trying to reduce usage of unit 9166 final usage = 1.02972 274 sec elapsed 10 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00257 0.05717\n",
      "starting to reduce usage for unit 6376 with usage 1.15971 . Total units tried = 45\n",
      "try to drop unit 6376 from 8 th HD.  usage down to 1.096090342531317\n",
      "try to drop unit 6376 from 16 th HD.  usage down to 1.0687717764424605\n",
      "all done trying to reduce usage of unit 6376 final usage = 1.02942 281 sec elapsed 14 2 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00256 0.05701\n",
      "starting to reduce usage for unit 2018 with usage 1.16143 . Total units tried = 46\n",
      "try to drop unit 2018 from 10 th HD.  usage down to 1.1028360818236282\n",
      "try to drop unit 2018 from 20 th HD.  usage down to 1.047771048272892\n",
      "all done trying to reduce usage of unit 2018 final usage = 1.02916 288 sec elapsed 17 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00255 0.0569\n",
      "starting to reduce usage for unit 1171 with usage 1.15913 . Total units tried = 47\n",
      "try to drop unit 1171 from 8 th HD.  usage down to 1.1318578631812868\n",
      "try to drop unit 1171 from 16 th HD.  usage down to 1.0803502841826784\n",
      "try to drop unit 1171 from 24 th HD.  usage down to 1.0473934882376539\n",
      "all done trying to reduce usage of unit 1171 final usage = 1.02846 294 sec elapsed 11 0 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00254 0.0567\n",
      "starting to reduce usage for unit 3731 with usage 1.16066 . Total units tried = 48\n",
      "try to drop unit 3731 from 8 th HD.  usage down to 1.0777828759446784\n",
      "all done trying to reduce usage of unit 3731 final usage = 1.02831 300 sec elapsed 10 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00253 0.05655\n",
      "starting to reduce usage for unit 7153 with usage 1.15643 . Total units tried = 49\n",
      "try to drop unit 7153 from 8 th HD.  usage down to 1.0728410345975357\n",
      "all done trying to reduce usage of unit 7153 final usage = 1.02044 306 sec elapsed 12 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00252 0.05647\n",
      "starting to reduce usage for unit 1597 with usage 1.15615 . Total units tried = 50\n",
      "try to drop unit 1597 from 5 th HD.  usage down to 1.100562331390454\n",
      "try to drop unit 1597 from 10 th HD.  usage down to 1.0325511837503396\n",
      "all done trying to reduce usage of unit 1597 final usage = 1.02221 312 sec elapsed 10 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00251 0.05634\n",
      "starting to reduce usage for unit 5628 with usage 1.15468 . Total units tried = 51\n",
      "try to drop unit 5628 from 8 th HD.  usage down to 1.0728326443745655\n",
      "try to drop unit 5628 from 16 th HD.  usage down to 1.0328112806633145\n",
      "all done trying to reduce usage of unit 5628 final usage = 1.02529 319 sec elapsed 12 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00251 0.05614\n",
      "starting to reduce usage for unit 7829 with usage 1.15177 . Total units tried = 52\n",
      "try to drop unit 7829 from 6 th HD.  usage down to 1.0816927198628332\n",
      "all done trying to reduce usage of unit 7829 final usage = 1.02064 325 sec elapsed 9 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00251 0.05598\n",
      "starting to reduce usage for unit 5943 with usage 1.15123 . Total units tried = 53\n",
      "try to drop unit 5943 from 9 th HD.  usage down to 1.0540637155229375\n",
      "all done trying to reduce usage of unit 5943 final usage = 1.02221 331 sec elapsed 11 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0025 0.05581\n",
      "starting to reduce usage for unit 325 with usage 1.14922 . Total units tried = 54\n",
      "try to drop unit 325 from 11 th HD.  usage down to 1.1426728606289034\n",
      "try to drop unit 325 from 22 th HD.  usage down to 1.1426728606289034\n",
      "try to drop unit 325 from 33 th HD.  usage down to 1.1231655921532944\n",
      "try to drop unit 325 from 44 th HD.  usage down to 1.101585938596622\n",
      "try to drop unit 325 from 55 th HD.  usage down to 1.101585938596622\n",
      "all done trying to reduce usage of unit 325 final usage = 1.10159 338 sec elapsed 6 2 successful, failed patches, couldn't start= 48\n",
      "current avg and SD of usage are 1.0025 0.05574\n",
      "starting to reduce usage for unit 3585 with usage 1.15286 . Total units tried = 55\n",
      "try to drop unit 3585 from 7 th HD.  usage down to 1.0677733399053992\n",
      "all done trying to reduce usage of unit 3585 final usage = 1.01727 344 sec elapsed 10 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00249 0.05559\n",
      "starting to reduce usage for unit 5978 with usage 1.14696 . Total units tried = 56\n",
      "try to drop unit 5978 from 7 th HD.  usage down to 1.0691577267003998\n",
      "all done trying to reduce usage of unit 5978 final usage = 1.01722 349 sec elapsed 10 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00248 0.05544\n",
      "starting to reduce usage for unit 8410 with usage 1.14287 . Total units tried = 57\n",
      "try to drop unit 8410 from 9 th HD.  usage down to 1.0571680980330647\n",
      "all done trying to reduce usage of unit 8410 final usage = 1.02216 355 sec elapsed 11 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00248 0.05527\n",
      "starting to reduce usage for unit 1767 with usage 1.14048 . Total units tried = 58\n",
      "try to drop unit 1767 from 11 th HD.  usage down to 1.0982550200653434\n",
      "try to drop unit 1767 from 22 th HD.  usage down to 1.0839077387348908\n",
      "all done trying to reduce usage of unit 1767 final usage = 1.02677 361 sec elapsed 11 0 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 1.00247 0.05516\n",
      "starting to reduce usage for unit 492 with usage 1.14006 . Total units tried = 59\n",
      "try to drop unit 492 from 8 th HD.  usage down to 1.1255568057082765\n",
      "try to drop unit 492 from 16 th HD.  usage down to 1.0335076691723888\n",
      "all done trying to reduce usage of unit 492 final usage = 1.02587 368 sec elapsed 8 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00246 0.055\n",
      "starting to reduce usage for unit 2341 with usage 1.13731 . Total units tried = 60\n",
      "try to drop unit 2341 from 8 th HD.  usage down to 1.0429802309396337\n",
      "all done trying to reduce usage of unit 2341 final usage = 1.01757 373 sec elapsed 8 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00245 0.05484\n",
      "starting to reduce usage for unit 8038 with usage 1.14329 . Total units tried = 61\n",
      "try to drop unit 8038 from 9 th HD.  usage down to 1.1432937371307847\n",
      "try to drop unit 8038 from 18 th HD.  usage down to 1.1276291907892937\n",
      "try to drop unit 8038 from 27 th HD.  usage down to 1.1138692250688615\n",
      "try to drop unit 8038 from 36 th HD.  usage down to 1.0759118562156684\n",
      "try to drop unit 8038 from 45 th HD.  usage down to 1.0462691983557428\n",
      "all done trying to reduce usage of unit 8038 final usage = 1.04627 380 sec elapsed 7 0 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 1.00245 0.05474\n",
      "starting to reduce usage for unit 7375 with usage 1.13891 . Total units tried = 62\n",
      "try to drop unit 7375 from 8 th HD.  usage down to 1.0826659857307204\n",
      "all done trying to reduce usage of unit 7375 final usage = 1.02863 386 sec elapsed 9 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00245 0.05458\n",
      "starting to reduce usage for unit 7906 with usage 1.13658 . Total units tried = 63\n",
      "try to drop unit 7906 from 8 th HD.  usage down to 1.035999565403331\n",
      "all done trying to reduce usage of unit 7906 final usage = 1.02296 392 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00244 0.05449\n",
      "starting to reduce usage for unit 6009 with usage 1.1358 . Total units tried = 64\n",
      "try to drop unit 6009 from 8 th HD.  usage down to 1.135801267991534\n",
      "try to drop unit 6009 from 16 th HD.  usage down to 1.1111759634857634\n",
      "try to drop unit 6009 from 24 th HD.  usage down to 1.0924573759721752\n",
      "try to drop unit 6009 from 32 th HD.  usage down to 1.0695856280734617\n",
      "try to drop unit 6009 from 40 th HD.  usage down to 1.0388858021155012\n",
      "all done trying to reduce usage of unit 6009 final usage = 1.03889 398 sec elapsed 9 0 successful, failed patches, couldn't start= 35\n",
      "current avg and SD of usage are 1.00243 0.05438\n",
      "starting to reduce usage for unit 5019 with usage 1.13567 . Total units tried = 65\n",
      "try to drop unit 5019 from 8 th HD.  usage down to 1.0534931803588992\n",
      "all done trying to reduce usage of unit 5019 final usage = 1.02369 405 sec elapsed 10 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00243 0.05427\n",
      "starting to reduce usage for unit 4599 with usage 1.13556 . Total units tried = 66\n",
      "try to drop unit 4599 from 7 th HD.  usage down to 1.0561025397120045\n",
      "all done trying to reduce usage of unit 4599 final usage = 1.02797 410 sec elapsed 9 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00242 0.05418\n",
      "starting to reduce usage for unit 4004 with usage 1.13516 . Total units tried = 67\n",
      "try to drop unit 4004 from 7 th HD.  usage down to 1.135155220820462\n",
      "try to drop unit 4004 from 14 th HD.  usage down to 1.135155220820462\n",
      "try to drop unit 4004 from 21 th HD.  usage down to 1.135155220820462\n",
      "try to drop unit 4004 from 28 th HD.  usage down to 1.135155220820462\n",
      "try to drop unit 4004 from 35 th HD.  usage down to 1.1228215930100736\n",
      "all done trying to reduce usage of unit 4004 final usage = 1.12282 416 sec elapsed 1 1 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 1.00242 0.05416\n",
      "starting to reduce usage for unit 4116 with usage 1.13459 . Total units tried = 68\n",
      "try to drop unit 4116 from 8 th HD.  usage down to 1.097768387131303\n",
      "try to drop unit 4116 from 16 th HD.  usage down to 1.0732521555245342\n",
      "try to drop unit 4116 from 24 th HD.  usage down to 1.031552747213231\n",
      "all done trying to reduce usage of unit 4116 final usage = 1.02645 422 sec elapsed 10 1 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.00242 0.05402\n",
      "starting to reduce usage for unit 8829 with usage 1.13321 . Total units tried = 69\n",
      "try to drop unit 8829 from 9 th HD.  usage down to 1.0587706306260936\n",
      "all done trying to reduce usage of unit 8829 final usage = 1.01791 428 sec elapsed 10 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00241 0.05389\n",
      "starting to reduce usage for unit 2967 with usage 1.13099 . Total units tried = 70\n",
      "try to drop unit 2967 from 8 th HD.  usage down to 1.0545167875649353\n",
      "all done trying to reduce usage of unit 2967 final usage = 1.0255 434 sec elapsed 9 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0024 0.05375\n",
      "starting to reduce usage for unit 1418 with usage 1.13988 . Total units tried = 71\n",
      "try to drop unit 1418 from 6 th HD.  usage down to 1.0987584334453964\n",
      "try to drop unit 1418 from 12 th HD.  usage down to 1.0679159736964323\n",
      "try to drop unit 1418 from 18 th HD.  usage down to 1.0405722369385852\n",
      "try to drop unit 1418 from 24 th HD.  usage down to 1.0405722369385852\n",
      "all done trying to reduce usage of unit 1418 final usage = 1.01601 440 sec elapsed 8 0 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.0024 0.05368\n",
      "starting to reduce usage for unit 3685 with usage 1.13031 . Total units tried = 72\n",
      "all done trying to reduce usage of unit 3685 final usage = 1.02529 446 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00239 0.05357\n",
      "starting to reduce usage for unit 391 with usage 1.13019 . Total units tried = 73\n",
      "try to drop unit 391 from 8 th HD.  usage down to 1.1301882088045099\n",
      "try to drop unit 391 from 16 th HD.  usage down to 1.1301882088045099\n",
      "try to drop unit 391 from 24 th HD.  usage down to 1.1118220106569374\n",
      "try to drop unit 391 from 32 th HD.  usage down to 1.0873393399421725\n",
      "try to drop unit 391 from 40 th HD.  usage down to 1.071792256722684\n",
      "all done trying to reduce usage of unit 391 final usage = 1.07179 453 sec elapsed 7 3 successful, failed patches, couldn't start= 33\n",
      "current avg and SD of usage are 1.00239 0.05348\n",
      "starting to reduce usage for unit 3451 with usage 1.12929 . Total units tried = 74\n",
      "try to drop unit 3451 from 6 th HD.  usage down to 1.1292904549432659\n",
      "try to drop unit 3451 from 12 th HD.  usage down to 1.1156060012298383\n",
      "try to drop unit 3451 from 18 th HD.  usage down to 1.0883209960329905\n",
      "all done trying to reduce usage of unit 3451 final usage = 1.02904 459 sec elapsed 6 0 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.00238 0.05338\n",
      "starting to reduce usage for unit 9357 with usage 1.12879 . Total units tried = 75\n",
      "try to drop unit 9357 from 7 th HD.  usage down to 1.1287870415633439\n",
      "try to drop unit 9357 from 14 th HD.  usage down to 1.1287870415633439\n",
      "try to drop unit 9357 from 21 th HD.  usage down to 1.1287870415633439\n",
      "try to drop unit 9357 from 28 th HD.  usage down to 1.1287870415633439\n",
      "try to drop unit 9357 from 35 th HD.  usage down to 1.1287870415633439\n",
      "all done trying to reduce usage of unit 9357 final usage = 1.12879 465 sec elapsed 0 0 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 1.00238 0.05338\n",
      "starting to reduce usage for unit 9359 with usage 1.12879 . Total units tried = 76\n",
      "try to drop unit 9359 from 7 th HD.  usage down to 1.1287870415633439\n",
      "try to drop unit 9359 from 14 th HD.  usage down to 1.1287870415633439\n",
      "try to drop unit 9359 from 21 th HD.  usage down to 1.1287870415633439\n",
      "try to drop unit 9359 from 28 th HD.  usage down to 1.1287870415633439\n",
      "try to drop unit 9359 from 35 th HD.  usage down to 1.1287870415633439\n",
      "all done trying to reduce usage of unit 9359 final usage = 1.12879 471 sec elapsed 0 0 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 1.00238 0.05338\n",
      "starting to reduce usage for unit 9557 with usage 1.12804 . Total units tried = 77\n",
      "try to drop unit 9557 from 9 th HD.  usage down to 1.0416126245906452\n",
      "all done trying to reduce usage of unit 9557 final usage = 1.01836 477 sec elapsed 8 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00238 0.05324\n",
      "starting to reduce usage for unit 8834 with usage 1.12798 . Total units tried = 78\n",
      "try to drop unit 8834 from 9 th HD.  usage down to 1.045975540550762\n",
      "all done trying to reduce usage of unit 8834 final usage = 1.02499 483 sec elapsed 10 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00237 0.05309\n",
      "starting to reduce usage for unit 6907 with usage 1.12756 . Total units tried = 79\n",
      "try to drop unit 6907 from 6 th HD.  usage down to 1.0780010217427467\n",
      "all done trying to reduce usage of unit 6907 final usage = 1.02312 489 sec elapsed 11 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00237 0.05299\n",
      "starting to reduce usage for unit 2709 with usage 1.12692 . Total units tried = 80\n",
      "try to drop unit 2709 from 8 th HD.  usage down to 1.0543405928819745\n",
      "all done trying to reduce usage of unit 2709 final usage = 1.02123 496 sec elapsed 10 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00236 0.05286\n",
      "starting to reduce usage for unit 4308 with usage 1.12606 . Total units tried = 81\n",
      "try to drop unit 4308 from 8 th HD.  usage down to 1.0517480139749449\n",
      "all done trying to reduce usage of unit 4308 final usage = 1.02765 502 sec elapsed 8 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00235 0.05281\n",
      "starting to reduce usage for unit 9786 with usage 1.12577 . Total units tried = 82\n",
      "try to drop unit 9786 from 8 th HD.  usage down to 1.052847133187875\n",
      "all done trying to reduce usage of unit 9786 final usage = 1.01981 508 sec elapsed 8 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00235 0.05267\n",
      "starting to reduce usage for unit 5632 with usage 1.12516 . Total units tried = 83\n",
      "try to drop unit 5632 from 7 th HD.  usage down to 1.0521591349019033\n",
      "all done trying to reduce usage of unit 5632 final usage = 1.02841 514 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00234 0.05259\n",
      "starting to reduce usage for unit 1369 with usage 1.12347 . Total units tried = 84\n",
      "all done trying to reduce usage of unit 1369 final usage = 1.02657 520 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00233 0.05249\n",
      "starting to reduce usage for unit 7465 with usage 1.12329 . Total units tried = 85\n",
      "try to drop unit 7465 from 8 th HD.  usage down to 1.035110201765328\n",
      "all done trying to reduce usage of unit 7465 final usage = 1.02036 526 sec elapsed 6 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00233 0.05241\n",
      "starting to reduce usage for unit 5698 with usage 1.12196 . Total units tried = 86\n",
      "try to drop unit 5698 from 9 th HD.  usage down to 1.040731651175506\n",
      "all done trying to reduce usage of unit 5698 final usage = 1.0219 532 sec elapsed 9 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00232 0.05233\n",
      "starting to reduce usage for unit 8146 with usage 1.13926 . Total units tried = 87\n",
      "try to drop unit 8146 from 7 th HD.  usage down to 1.113181226782822\n",
      "try to drop unit 8146 from 14 th HD.  usage down to 1.0788484342657474\n",
      "all done trying to reduce usage of unit 8146 final usage = 1.02572 538 sec elapsed 10 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00232 0.05218\n",
      "starting to reduce usage for unit 3562 with usage 1.12103 . Total units tried = 88\n",
      "try to drop unit 3562 from 6 th HD.  usage down to 1.0625797918682394\n",
      "all done trying to reduce usage of unit 3562 final usage = 1.02139 545 sec elapsed 8 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00231 0.0521\n",
      "starting to reduce usage for unit 1057 with usage 1.12017 . Total units tried = 89\n",
      "try to drop unit 1057 from 5 th HD.  usage down to 1.0719684514055048\n",
      "all done trying to reduce usage of unit 1057 final usage = 1.02962 551 sec elapsed 6 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00231 0.05198\n",
      "starting to reduce usage for unit 7683 with usage 1.11943 . Total units tried = 90\n",
      "try to drop unit 7683 from 6 th HD.  usage down to 1.0405302858234826\n",
      "all done trying to reduce usage of unit 7683 final usage = 1.0268 557 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0023 0.05191\n",
      "starting to reduce usage for unit 9831 with usage 1.1193 . Total units tried = 91\n",
      "try to drop unit 9831 from 8 th HD.  usage down to 1.038701217209448\n",
      "all done trying to reduce usage of unit 9831 final usage = 1.02264 564 sec elapsed 7 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.0023 0.0518\n",
      "starting to reduce usage for unit 5035 with usage 1.11968 . Total units tried = 92\n",
      "try to drop unit 5035 from 9 th HD.  usage down to 1.0344138132563474\n",
      "all done trying to reduce usage of unit 5035 final usage = 1.02028 570 sec elapsed 9 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.0517\n",
      "starting to reduce usage for unit 7085 with usage 1.11764 . Total units tried = 93\n",
      "try to drop unit 7085 from 7 th HD.  usage down to 1.0465209050456348\n",
      "all done trying to reduce usage of unit 7085 final usage = 1.01998 577 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.05159\n",
      "starting to reduce usage for unit 4001 with usage 1.12133 . Total units tried = 94\n",
      "try to drop unit 4001 from 3 th HD.  usage down to 1.09686224304725\n",
      "try to drop unit 4001 from 6 th HD.  usage down to 1.09686224304725\n",
      "try to drop unit 4001 from 9 th HD.  usage down to 1.076037709560602\n",
      "try to drop unit 4001 from 12 th HD.  usage down to 1.0605913090171217\n",
      "try to drop unit 4001 from 15 th HD.  usage down to 1.0605913090171217\n",
      "try to drop unit 4001 from 18 th HD.  usage down to 1.0360750774103638\n",
      "all done trying to reduce usage of unit 4001 final usage = 1.03608 583 sec elapsed 7 5 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00229 0.05148\n",
      "starting to reduce usage for unit 6273 with usage 1.13641 . Total units tried = 95\n",
      "try to drop unit 6273 from 4 th HD.  usage down to 1.1138776152918077\n",
      "try to drop unit 6273 from 8 th HD.  usage down to 1.0714734282484732\n",
      "try to drop unit 6273 from 12 th HD.  usage down to 1.0321484530462424\n",
      "all done trying to reduce usage of unit 6273 final usage = 1.01918 590 sec elapsed 12 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00229 0.05134\n",
      "starting to reduce usage for unit 524 with usage 1.11681 . Total units tried = 96\n",
      "try to drop unit 524 from 7 th HD.  usage down to 1.0371406357314525\n",
      "all done trying to reduce usage of unit 524 final usage = 1.02844 597 sec elapsed 7 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00228 0.05125\n",
      "starting to reduce usage for unit 5988 with usage 1.11718 . Total units tried = 97\n",
      "all done trying to reduce usage of unit 5988 final usage = 1.02777 603 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00228 0.05115\n",
      "starting to reduce usage for unit 3682 with usage 1.11669 . Total units tried = 98\n",
      "try to drop unit 3682 from 8 th HD.  usage down to 1.0443310568426656\n",
      "all done trying to reduce usage of unit 3682 final usage = 1.02129 609 sec elapsed 7 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00228 0.05103\n",
      "starting to reduce usage for unit 6238 with usage 1.11666 . Total units tried = 99\n",
      "try to drop unit 6238 from 7 th HD.  usage down to 1.0540049839619503\n",
      "all done trying to reduce usage of unit 6238 final usage = 1.02841 616 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00227 0.05088\n",
      "starting to reduce usage for unit 6710 with usage 1.11562 . Total units tried = 100\n",
      "all done trying to reduce usage of unit 6710 final usage = 1.01564 622 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00227 0.05074\n",
      "starting to reduce usage for unit 4465 with usage 1.11563 . Total units tried = 101\n",
      "try to drop unit 4465 from 9 th HD.  usage down to 1.031997429032276\n",
      "all done trying to reduce usage of unit 4465 final usage = 1.02421 628 sec elapsed 9 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00226 0.05068\n",
      "starting to reduce usage for unit 8528 with usage 1.1215 . Total units tried = 102\n",
      "try to drop unit 8528 from 4 th HD.  usage down to 1.0760209291147023\n",
      "try to drop unit 8528 from 8 th HD.  usage down to 1.0438192532397026\n",
      "all done trying to reduce usage of unit 8528 final usage = 1.02289 634 sec elapsed 9 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00226 0.0506\n",
      "starting to reduce usage for unit 4751 with usage 1.11249 . Total units tried = 103\n",
      "try to drop unit 4751 from 6 th HD.  usage down to 1.0464957343766392\n",
      "all done trying to reduce usage of unit 4751 final usage = 1.01838 640 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00226 0.05055\n",
      "starting to reduce usage for unit 3989 with usage 1.11231 . Total units tried = 104\n",
      "try to drop unit 3989 from 7 th HD.  usage down to 1.0860891967149353\n",
      "try to drop unit 3989 from 14 th HD.  usage down to 1.0860891967149353\n",
      "try to drop unit 3989 from 21 th HD.  usage down to 1.0860891967149353\n",
      "try to drop unit 3989 from 28 th HD.  usage down to 1.0766585860626379\n",
      "try to drop unit 3989 from 35 th HD.  usage down to 1.0766585860626379\n",
      "all done trying to reduce usage of unit 3989 final usage = 1.07666 646 sec elapsed 3 0 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 1.00225 0.05051\n",
      "starting to reduce usage for unit 1183 with usage 1.11386 . Total units tried = 105\n",
      "try to drop unit 1183 from 7 th HD.  usage down to 1.0435927172185668\n",
      "all done trying to reduce usage of unit 1183 final usage = 1.02773 652 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00225 0.05042\n",
      "starting to reduce usage for unit 8972 with usage 1.11165 . Total units tried = 106\n",
      "try to drop unit 8972 from 9 th HD.  usage down to 1.0449603235677316\n",
      "all done trying to reduce usage of unit 8972 final usage = 1.0239 659 sec elapsed 10 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00225 0.05031\n",
      "starting to reduce usage for unit 6221 with usage 1.11162 . Total units tried = 107\n",
      "try to drop unit 6221 from 6 th HD.  usage down to 1.0609436983831761\n",
      "all done trying to reduce usage of unit 6221 final usage = 1.02811 665 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00225 0.0502\n",
      "starting to reduce usage for unit 4510 with usage 1.11155 . Total units tried = 108\n",
      "try to drop unit 4510 from 8 th HD.  usage down to 1.0623364754012\n",
      "all done trying to reduce usage of unit 4510 final usage = 1.02572 671 sec elapsed 8 3 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00224 0.05013\n",
      "starting to reduce usage for unit 8763 with usage 1.11149 . Total units tried = 109\n",
      "all done trying to reduce usage of unit 8763 final usage = 1.02997 677 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.05003\n",
      "starting to reduce usage for unit 7891 with usage 1.11167 . Total units tried = 110\n",
      "all done trying to reduce usage of unit 7891 final usage = 1.02692 683 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00224 0.04994\n",
      "starting to reduce usage for unit 7852 with usage 1.11087 . Total units tried = 111\n",
      "try to drop unit 7852 from 8 th HD.  usage down to 1.1037086650155323\n",
      "try to drop unit 7852 from 16 th HD.  usage down to 1.0832700817868914\n",
      "try to drop unit 7852 from 24 th HD.  usage down to 1.0832700817868914\n",
      "try to drop unit 7852 from 32 th HD.  usage down to 1.0720523536355455\n",
      "try to drop unit 7852 from 40 th HD.  usage down to 1.0598026280551642\n",
      "all done trying to reduce usage of unit 7852 final usage = 1.03952 690 sec elapsed 7 0 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 1.00224 0.04987\n",
      "starting to reduce usage for unit 2970 with usage 1.11081 . Total units tried = 112\n",
      "try to drop unit 2970 from 6 th HD.  usage down to 1.0712636726733824\n",
      "all done trying to reduce usage of unit 2970 final usage = 1.01549 696 sec elapsed 7 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00223 0.04972\n",
      "starting to reduce usage for unit 6162 with usage 1.10851 . Total units tried = 113\n",
      "try to drop unit 6162 from 7 th HD.  usage down to 1.0598445791701518\n",
      "all done trying to reduce usage of unit 6162 final usage = 1.02432 702 sec elapsed 7 2 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00222 0.04963\n",
      "starting to reduce usage for unit 1944 with usage 1.11806 . Total units tried = 114\n",
      "try to drop unit 1944 from 7 th HD.  usage down to 1.079225994300797\n",
      "all done trying to reduce usage of unit 1944 final usage = 1.02026 708 sec elapsed 5 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00222 0.04957\n",
      "starting to reduce usage for unit 6279 with usage 1.11717 . Total units tried = 115\n",
      "all done trying to reduce usage of unit 6279 final usage = 1.02767 714 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00221 0.04944\n",
      "starting to reduce usage for unit 3093 with usage 1.10779 . Total units tried = 116\n",
      "try to drop unit 3093 from 5 th HD.  usage down to 1.084797102373009\n",
      "try to drop unit 3093 from 10 th HD.  usage down to 1.0395150688405814\n",
      "all done trying to reduce usage of unit 3093 final usage = 1.02991 721 sec elapsed 10 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00221 0.04935\n",
      "starting to reduce usage for unit 7805 with usage 1.10745 . Total units tried = 117\n",
      "try to drop unit 7805 from 7 th HD.  usage down to 1.0875658759629727\n",
      "all done trying to reduce usage of unit 7805 final usage = 1.02569 727 sec elapsed 6 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.0022 0.04927\n",
      "starting to reduce usage for unit 3934 with usage 1.10667 . Total units tried = 118\n",
      "all done trying to reduce usage of unit 3934 final usage = 1.02875 733 sec elapsed 6 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.0022 0.04917\n",
      "starting to reduce usage for unit 2793 with usage 1.10653 . Total units tried = 119\n",
      "try to drop unit 2793 from 9 th HD.  usage down to 1.0541140568610214\n",
      "all done trying to reduce usage of unit 2793 final usage = 1.02345 740 sec elapsed 6 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00219 0.04908\n",
      "starting to reduce usage for unit 2743 with usage 1.10641 . Total units tried = 120\n",
      "try to drop unit 2743 from 7 th HD.  usage down to 1.037778292679473\n",
      "all done trying to reduce usage of unit 2743 final usage = 1.02315 746 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00219 0.04902\n",
      "starting to reduce usage for unit 8816 with usage 1.10588 . Total units tried = 121\n",
      "try to drop unit 8816 from 7 th HD.  usage down to 1.0677397790133893\n",
      "try to drop unit 8816 from 14 th HD.  usage down to 1.0328951828933006\n",
      "all done trying to reduce usage of unit 8816 final usage = 1.02132 752 sec elapsed 7 0 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00218 0.04894\n",
      "starting to reduce usage for unit 7155 with usage 1.10579 . Total units tried = 122\n",
      "try to drop unit 7155 from 8 th HD.  usage down to 1.0703155774744675\n",
      "all done trying to reduce usage of unit 7155 final usage = 1.02247 758 sec elapsed 7 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00218 0.04888\n",
      "starting to reduce usage for unit 3001 with usage 1.10551 . Total units tried = 123\n",
      "all done trying to reduce usage of unit 3001 final usage = 1.02943 764 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00218 0.04875\n",
      "starting to reduce usage for unit 2773 with usage 1.10551 . Total units tried = 124\n",
      "all done trying to reduce usage of unit 2773 final usage = 1.02013 770 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00217 0.04868\n",
      "starting to reduce usage for unit 3586 with usage 1.10548 . Total units tried = 125\n",
      "all done trying to reduce usage of unit 3586 final usage = 1.02728 777 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00217 0.0486\n",
      "starting to reduce usage for unit 2457 with usage 1.10524 . Total units tried = 126\n",
      "all done trying to reduce usage of unit 2457 final usage = 1.02708 783 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00217 0.04851\n",
      "starting to reduce usage for unit 2353 with usage 1.10522 . Total units tried = 127\n",
      "all done trying to reduce usage of unit 2353 final usage = 1.02672 789 sec elapsed 5 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00217 0.04843\n",
      "starting to reduce usage for unit 9671 with usage 1.10487 . Total units tried = 128\n",
      "all done trying to reduce usage of unit 9671 final usage = 1.02229 795 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00216 0.04832\n",
      "starting to reduce usage for unit 7167 with usage 1.1048 . Total units tried = 129\n",
      "all done trying to reduce usage of unit 7167 final usage = 1.02966 801 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00216 0.04825\n",
      "starting to reduce usage for unit 3131 with usage 1.10477 . Total units tried = 130\n",
      "try to drop unit 3131 from 8 th HD.  usage down to 1.0510767961348448\n",
      "all done trying to reduce usage of unit 3131 final usage = 1.02471 807 sec elapsed 6 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00215 0.04818\n",
      "starting to reduce usage for unit 3962 with usage 1.10411 . Total units tried = 131\n",
      "try to drop unit 3962 from 8 th HD.  usage down to 1.0397164341925629\n",
      "all done trying to reduce usage of unit 3962 final usage = 1.02447 814 sec elapsed 6 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00215 0.04807\n",
      "starting to reduce usage for unit 5922 with usage 1.10491 . Total units tried = 132\n",
      "all done trying to reduce usage of unit 5922 final usage = 1.02423 820 sec elapsed 6 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00215 0.048\n",
      "starting to reduce usage for unit 2842 with usage 1.10352 . Total units tried = 133\n",
      "all done trying to reduce usage of unit 2842 final usage = 1.01807 827 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00214 0.04787\n",
      "starting to reduce usage for unit 5979 with usage 1.10331 . Total units tried = 134\n",
      "try to drop unit 5979 from 7 th HD.  usage down to 1.0705924548335584\n",
      "all done trying to reduce usage of unit 5979 final usage = 1.02254 834 sec elapsed 7 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00214 0.04777\n",
      "starting to reduce usage for unit 9746 with usage 1.10317 . Total units tried = 135\n",
      "all done trying to reduce usage of unit 9746 final usage = 1.02664 840 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00214 0.04766\n",
      "starting to reduce usage for unit 5024 with usage 1.10266 . Total units tried = 136\n",
      "try to drop unit 5024 from 7 th HD.  usage down to 1.0475696829206353\n",
      "all done trying to reduce usage of unit 5024 final usage = 1.02319 846 sec elapsed 7 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00213 0.04757\n",
      "starting to reduce usage for unit 1963 with usage 1.10232 . Total units tried = 137\n",
      "try to drop unit 1963 from 6 th HD.  usage down to 1.0302606528712368\n",
      "all done trying to reduce usage of unit 1963 final usage = 1.0168 853 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00213 0.04754\n",
      "starting to reduce usage for unit 8951 with usage 1.10228 . Total units tried = 138\n",
      "try to drop unit 8951 from 2 th HD.  usage down to 1.0917777679091814\n",
      "try to drop unit 8951 from 4 th HD.  usage down to 1.0795448227747955\n",
      "try to drop unit 8951 from 6 th HD.  usage down to 1.0795448227747955\n",
      "try to drop unit 8951 from 8 th HD.  usage down to 1.0795448227747955\n",
      "try to drop unit 8951 from 10 th HD.  usage down to 1.0674293407624178\n",
      "try to drop unit 8951 from 12 th HD.  usage down to 1.0674293407624178\n",
      "try to drop unit 8951 from 14 th HD.  usage down to 1.0674293407624178\n",
      "all done trying to reduce usage of unit 8951 final usage = 1.06743 859 sec elapsed 3 0 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00213 0.04748\n",
      "starting to reduce usage for unit 4715 with usage 1.10364 . Total units tried = 139\n",
      "all done trying to reduce usage of unit 4715 final usage = 1.01426 865 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00212 0.04744\n",
      "starting to reduce usage for unit 3401 with usage 1.10149 . Total units tried = 140\n",
      "try to drop unit 3401 from 7 th HD.  usage down to 1.074309323622546\n",
      "try to drop unit 3401 from 14 th HD.  usage down to 1.0535183510278945\n",
      "all done trying to reduce usage of unit 3401 final usage = 1.02753 871 sec elapsed 6 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00211 0.04739\n",
      "starting to reduce usage for unit 9783 with usage 1.10105 . Total units tried = 141\n",
      "all done trying to reduce usage of unit 9783 final usage = 1.01922 877 sec elapsed 5 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00211 0.04734\n",
      "starting to reduce usage for unit 6043 with usage 1.10098 . Total units tried = 142\n",
      "all done trying to reduce usage of unit 6043 final usage = 1.02298 883 sec elapsed 6 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0021 0.04726\n",
      "starting to reduce usage for unit 4802 with usage 1.10084 . Total units tried = 143\n",
      "all done trying to reduce usage of unit 4802 final usage = 1.01904 890 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0021 0.04718\n",
      "starting to reduce usage for unit 8435 with usage 1.10051 . Total units tried = 144\n",
      "try to drop unit 8435 from 7 th HD.  usage down to 1.064475982266281\n",
      "all done trying to reduce usage of unit 8435 final usage = 1.01674 896 sec elapsed 8 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00209 0.04713\n",
      "starting to reduce usage for unit 7817 with usage 1.10044 . Total units tried = 145\n",
      "try to drop unit 7817 from 8 th HD.  usage down to 1.0891767987790733\n",
      "try to drop unit 7817 from 16 th HD.  usage down to 1.0891767987790733\n",
      "try to drop unit 7817 from 24 th HD.  usage down to 1.0891767987790733\n",
      "try to drop unit 7817 from 32 th HD.  usage down to 1.0891767987790733\n",
      "try to drop unit 7817 from 40 th HD.  usage down to 1.0778248270597237\n",
      "all done trying to reduce usage of unit 7817 final usage = 1.07782 902 sec elapsed 2 0 successful, failed patches, couldn't start= 40\n",
      "current avg and SD of usage are 1.00209 0.0471\n",
      "starting to reduce usage for unit 9546 with usage 1.10025 . Total units tried = 146\n",
      "all done trying to reduce usage of unit 9546 final usage = 1.01702 909 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00209 0.04706\n",
      "starting to reduce usage for unit 7036 with usage 1.10025 . Total units tried = 147\n",
      "all done trying to reduce usage of unit 7036 final usage = 1.02044 915 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00209 0.04698\n",
      "starting to reduce usage for unit 5229 with usage 1.10017 . Total units tried = 148\n",
      "try to drop unit 5229 from 5 th HD.  usage down to 1.0492896786358092\n",
      "all done trying to reduce usage of unit 5229 final usage = 1.02312 921 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00208 0.04694\n",
      "starting to reduce usage for unit 2803 with usage 1.0992 . Total units tried = 149\n",
      "all done trying to reduce usage of unit 2803 final usage = 1.02542 927 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00209 0.04689\n",
      "starting to reduce usage for unit 8035 with usage 1.1018 . Total units tried = 150\n",
      "try to drop unit 8035 from 8 th HD.  usage down to 1.101804084394486\n",
      "try to drop unit 8035 from 16 th HD.  usage down to 1.0667161718073808\n",
      "try to drop unit 8035 from 24 th HD.  usage down to 1.0480814865237948\n",
      "all done trying to reduce usage of unit 8035 final usage = 1.02025 933 sec elapsed 7 0 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 1.00208 0.04681\n",
      "starting to reduce usage for unit 422 with usage 1.09911 . Total units tried = 151\n",
      "try to drop unit 422 from 7 th HD.  usage down to 1.0591985319990573\n",
      "all done trying to reduce usage of unit 422 final usage = 1.01985 939 sec elapsed 6 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00208 0.04672\n",
      "starting to reduce usage for unit 5948 with usage 1.09719 . Total units tried = 152\n",
      "try to drop unit 5948 from 9 th HD.  usage down to 1.0498350431308796\n",
      "all done trying to reduce usage of unit 5948 final usage = 1.0239 945 sec elapsed 8 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00207 0.04662\n",
      "starting to reduce usage for unit 4119 with usage 1.09692 . Total units tried = 153\n",
      "all done trying to reduce usage of unit 4119 final usage = 1.02513 952 sec elapsed 7 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00207 0.04653\n",
      "starting to reduce usage for unit 6259 with usage 1.09644 . Total units tried = 154\n",
      "all done trying to reduce usage of unit 6259 final usage = 1.02016 958 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00207 0.04644\n",
      "starting to reduce usage for unit 7838 with usage 1.09622 . Total units tried = 155\n",
      "all done trying to reduce usage of unit 7838 final usage = 1.02222 964 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00206 0.04638\n",
      "starting to reduce usage for unit 6999 with usage 1.09569 . Total units tried = 156\n",
      "all done trying to reduce usage of unit 6999 final usage = 1.01947 970 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00206 0.04633\n",
      "starting to reduce usage for unit 5953 with usage 1.11147 . Total units tried = 157\n",
      "all done trying to reduce usage of unit 5953 final usage = 1.02396 977 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00205 0.04628\n",
      "starting to reduce usage for unit 1124 with usage 1.09548 . Total units tried = 158\n",
      "try to drop unit 1124 from 6 th HD.  usage down to 1.0428711580405583\n",
      "all done trying to reduce usage of unit 1124 final usage = 1.02592 983 sec elapsed 5 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00205 0.04621\n",
      "starting to reduce usage for unit 7094 with usage 1.09512 . Total units tried = 159\n",
      "try to drop unit 7094 from 9 th HD.  usage down to 1.051999720664878\n",
      "all done trying to reduce usage of unit 7094 final usage = 1.01414 989 sec elapsed 7 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00204 0.04618\n",
      "starting to reduce usage for unit 1536 with usage 1.09499 . Total units tried = 160\n",
      "all done trying to reduce usage of unit 1536 final usage = 1.02317 995 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00204 0.04609\n",
      "starting to reduce usage for unit 4097 with usage 1.09482 . Total units tried = 161\n",
      "try to drop unit 4097 from 9 th HD.  usage down to 1.0349927386434716\n",
      "all done trying to reduce usage of unit 4097 final usage = 1.01915 1001 sec elapsed 6 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00203 0.04604\n",
      "starting to reduce usage for unit 5417 with usage 1.09916 . Total units tried = 162\n",
      "all done trying to reduce usage of unit 5417 final usage = 1.02544 1007 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00203 0.04599\n",
      "starting to reduce usage for unit 6700 with usage 1.09336 . Total units tried = 163\n",
      "all done trying to reduce usage of unit 6700 final usage = 1.02921 1013 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00202 0.04593\n",
      "starting to reduce usage for unit 9437 with usage 1.09318 . Total units tried = 164\n",
      "try to drop unit 9437 from 8 th HD.  usage down to 1.093178935150292\n",
      "try to drop unit 9437 from 16 th HD.  usage down to 1.0818101829849371\n",
      "try to drop unit 9437 from 24 th HD.  usage down to 1.0696359694115531\n",
      "try to drop unit 9437 from 32 th HD.  usage down to 1.0505649925319518\n",
      "try to drop unit 9437 from 40 th HD.  usage down to 1.043718570563736\n",
      "all done trying to reduce usage of unit 9437 final usage = 1.03302 1020 sec elapsed 9 0 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 1.00202 0.04585\n",
      "starting to reduce usage for unit 2993 with usage 1.09281 . Total units tried = 165\n",
      "all done trying to reduce usage of unit 2993 final usage = 1.02383 1026 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00201 0.04576\n",
      "starting to reduce usage for unit 327 with usage 1.0927 . Total units tried = 166\n",
      "try to drop unit 327 from 5 th HD.  usage down to 1.082070279898021\n",
      "try to drop unit 327 from 10 th HD.  usage down to 1.0718425980606985\n",
      "try to drop unit 327 from 15 th HD.  usage down to 1.0652898338974919\n",
      "try to drop unit 327 from 20 th HD.  usage down to 1.050615333870031\n",
      "try to drop unit 327 from 25 th HD.  usage down to 1.0418139899427534\n",
      "all done trying to reduce usage of unit 327 final usage = 1.04181 1032 sec elapsed 8 2 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00201 0.04568\n",
      "starting to reduce usage for unit 9748 with usage 1.0927 . Total units tried = 167\n",
      "all done trying to reduce usage of unit 9748 final usage = 1.02044 1038 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00201 0.0456\n",
      "starting to reduce usage for unit 5771 with usage 1.0926 . Total units tried = 168\n",
      "all done trying to reduce usage of unit 5771 final usage = 1.02778 1045 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.002 0.04554\n",
      "starting to reduce usage for unit 6385 with usage 1.09252 . Total units tried = 169\n",
      "try to drop unit 6385 from 9 th HD.  usage down to 1.0570086837961148\n",
      "all done trying to reduce usage of unit 6385 final usage = 1.02517 1051 sec elapsed 6 4 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.002 0.04546\n",
      "starting to reduce usage for unit 1732 with usage 1.09208 . Total units tried = 170\n",
      "all done trying to reduce usage of unit 1732 final usage = 1.01757 1058 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.002 0.04542\n",
      "starting to reduce usage for unit 1147 with usage 1.09199 . Total units tried = 171\n",
      "try to drop unit 1147 from 1 th HD.  usage down to 1.0919875234841707\n",
      "try to drop unit 1147 from 2 th HD.  usage down to 1.0770697069897024\n",
      "try to drop unit 1147 from 3 th HD.  usage down to 1.0537784479409726\n",
      "try to drop unit 1147 from 4 th HD.  usage down to 1.0421663793086087\n",
      "try to drop unit 1147 from 5 th HD.  usage down to 1.0308563587042527\n",
      "all done trying to reduce usage of unit 1147 final usage = 1.01821 1064 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.002 0.04534\n",
      "starting to reduce usage for unit 7797 with usage 1.10805 . Total units tried = 172\n",
      "try to drop unit 7797 from 6 th HD.  usage down to 1.051563429068862\n",
      "all done trying to reduce usage of unit 7797 final usage = 1.02313 1071 sec elapsed 6 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00199 0.04527\n",
      "starting to reduce usage for unit 4148 with usage 1.09155 . Total units tried = 173\n",
      "all done trying to reduce usage of unit 4148 final usage = 1.02066 1077 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00199 0.04525\n",
      "starting to reduce usage for unit 249 with usage 1.09132 . Total units tried = 174\n",
      "all done trying to reduce usage of unit 249 final usage = 1.02565 1084 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00198 0.04518\n",
      "starting to reduce usage for unit 2723 with usage 1.09062 . Total units tried = 175\n",
      "try to drop unit 2723 from 5 th HD.  usage down to 1.0523437198078982\n",
      "all done trying to reduce usage of unit 2723 final usage = 1.02423 1089 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00198 0.0451\n",
      "starting to reduce usage for unit 1030 with usage 1.09002 . Total units tried = 176\n",
      "all done trying to reduce usage of unit 1030 final usage = 1.02351 1096 sec elapsed 6 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00198 0.04499\n",
      "starting to reduce usage for unit 3874 with usage 1.08993 . Total units tried = 177\n",
      "all done trying to reduce usage of unit 3874 final usage = 1.02335 1102 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00197 0.0449\n",
      "starting to reduce usage for unit 8790 with usage 1.08972 . Total units tried = 178\n",
      "try to drop unit 8790 from 5 th HD.  usage down to 1.060599699240183\n",
      "try to drop unit 8790 from 10 th HD.  usage down to 1.0530484985399449\n",
      "all done trying to reduce usage of unit 8790 final usage = 1.02825 1108 sec elapsed 5 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00197 0.04484\n",
      "starting to reduce usage for unit 8211 with usage 1.09199 . Total units tried = 179\n",
      "all done trying to reduce usage of unit 8211 final usage = 1.02293 1115 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00197 0.0448\n",
      "starting to reduce usage for unit 1004 with usage 1.08965 . Total units tried = 180\n",
      "all done trying to reduce usage of unit 1004 final usage = 1.02529 1121 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00196 0.04474\n",
      "starting to reduce usage for unit 3766 with usage 1.08952 . Total units tried = 181\n",
      "all done trying to reduce usage of unit 3766 final usage = 1.02498 1127 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00196 0.04472\n",
      "starting to reduce usage for unit 4271 with usage 1.08947 . Total units tried = 182\n",
      "all done trying to reduce usage of unit 4271 final usage = 1.02824 1132 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00195 0.04467\n",
      "starting to reduce usage for unit 9901 with usage 1.08933 . Total units tried = 183\n",
      "all done trying to reduce usage of unit 9901 final usage = 1.02527 1138 sec elapsed 5 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00195 0.04461\n",
      "starting to reduce usage for unit 6505 with usage 1.08932 . Total units tried = 184\n",
      "try to drop unit 6505 from 6 th HD.  usage down to 1.031494015652307\n",
      "all done trying to reduce usage of unit 6505 final usage = 1.02074 1144 sec elapsed 5 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00195 0.04452\n",
      "starting to reduce usage for unit 8808 with usage 1.09515 . Total units tried = 185\n",
      "try to drop unit 8808 from 6 th HD.  usage down to 1.031871575687306\n",
      "all done trying to reduce usage of unit 8808 final usage = 1.01684 1150 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00194 0.04446\n",
      "starting to reduce usage for unit 8626 with usage 1.08888 . Total units tried = 186\n",
      "try to drop unit 8626 from 8 th HD.  usage down to 1.0528303527419558\n",
      "all done trying to reduce usage of unit 8626 final usage = 1.01144 1156 sec elapsed 7 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00194 0.04437\n",
      "starting to reduce usage for unit 1825 with usage 1.08874 . Total units tried = 187\n",
      "try to drop unit 1825 from 7 th HD.  usage down to 1.0476451949277543\n",
      "all done trying to reduce usage of unit 1825 final usage = 1.02692 1163 sec elapsed 5 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00194 0.04431\n",
      "starting to reduce usage for unit 2841 with usage 1.0887 . Total units tried = 188\n",
      "try to drop unit 2841 from 9 th HD.  usage down to 1.0886985560680615\n",
      "try to drop unit 2841 from 18 th HD.  usage down to 1.0573946340540774\n",
      "all done trying to reduce usage of unit 2841 final usage = 1.02172 1169 sec elapsed 5 0 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 1.00193 0.04421\n",
      "starting to reduce usage for unit 8532 with usage 1.08805 . Total units tried = 189\n",
      "try to drop unit 8532 from 9 th HD.  usage down to 1.073252155524551\n",
      "try to drop unit 8532 from 18 th HD.  usage down to 1.0594754093581236\n",
      "all done trying to reduce usage of unit 8532 final usage = 1.00526 1175 sec elapsed 4 0 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 1.00193 0.04412\n",
      "starting to reduce usage for unit 8954 with usage 1.08793 . Total units tried = 190\n",
      "try to drop unit 8954 from 8 th HD.  usage down to 1.0879266555520541\n",
      "try to drop unit 8954 from 16 th HD.  usage down to 1.0736968373436093\n",
      "try to drop unit 8954 from 24 th HD.  usage down to 1.0736968373436093\n",
      "all done trying to reduce usage of unit 8954 final usage = 1.02909 1181 sec elapsed 5 0 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 1.00193 0.04405\n",
      "starting to reduce usage for unit 2990 with usage 1.08764 . Total units tried = 191\n",
      "all done trying to reduce usage of unit 2990 final usage = 1.01663 1186 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00192 0.04397\n",
      "starting to reduce usage for unit 5419 with usage 1.0876 . Total units tried = 192\n",
      "all done trying to reduce usage of unit 5419 final usage = 1.02116 1193 sec elapsed 6 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00191 0.04393\n",
      "starting to reduce usage for unit 9639 with usage 1.08755 . Total units tried = 193\n",
      "all done trying to reduce usage of unit 9639 final usage = 1.02126 1199 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00191 0.04387\n",
      "starting to reduce usage for unit 1586 with usage 1.08749 . Total units tried = 194\n",
      "try to drop unit 1586 from 6 th HD.  usage down to 1.0342627892423306\n",
      "all done trying to reduce usage of unit 1586 final usage = 1.02013 1205 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00191 0.04382\n",
      "starting to reduce usage for unit 4816 with usage 1.08735 . Total units tried = 195\n",
      "all done trying to reduce usage of unit 4816 final usage = 1.02909 1211 sec elapsed 4 1 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0019 0.04373\n",
      "starting to reduce usage for unit 6530 with usage 1.08704 . Total units tried = 196\n",
      "all done trying to reduce usage of unit 6530 final usage = 1.02418 1217 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0019 0.04366\n",
      "starting to reduce usage for unit 8497 with usage 1.087 . Total units tried = 197\n",
      "try to drop unit 8497 from 7 th HD.  usage down to 1.031150016509187\n",
      "all done trying to reduce usage of unit 8497 final usage = 1.01951 1223 sec elapsed 6 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0019 0.04361\n",
      "starting to reduce usage for unit 2369 with usage 1.0864 . Total units tried = 198\n",
      "try to drop unit 2369 from 6 th HD.  usage down to 1.0350346897583105\n",
      "all done trying to reduce usage of unit 2369 final usage = 1.02328 1229 sec elapsed 5 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0019 0.04356\n",
      "starting to reduce usage for unit 6290 with usage 1.08618 . Total units tried = 199\n",
      "try to drop unit 6290 from 7 th HD.  usage down to 1.0539378621779343\n",
      "all done trying to reduce usage of unit 6290 final usage = 1.01792 1236 sec elapsed 7 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0019 0.04346\n",
      "starting to reduce usage for unit 4935 with usage 1.11392 . Total units tried = 200\n",
      "try to drop unit 4935 from 5 th HD.  usage down to 1.0619337446971084\n",
      "all done trying to reduce usage of unit 4935 final usage = 1.02654 1242 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00189 0.04333\n",
      "starting to reduce usage for unit 8895 with usage 1.08565 . Total units tried = 201\n",
      "all done trying to reduce usage of unit 8895 final usage = 1.02476 1248 sec elapsed 6 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00189 0.04325\n",
      "starting to reduce usage for unit 7042 with usage 1.08546 . Total units tried = 202\n",
      "all done trying to reduce usage of unit 7042 final usage = 1.01964 1254 sec elapsed 6 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00188 0.04321\n",
      "starting to reduce usage for unit 6510 with usage 1.08535 . Total units tried = 203\n",
      "all done trying to reduce usage of unit 6510 final usage = 1.02774 1260 sec elapsed 5 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00188 0.04313\n",
      "starting to reduce usage for unit 5790 with usage 1.08535 . Total units tried = 204\n",
      "all done trying to reduce usage of unit 5790 final usage = 1.02534 1266 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00188 0.04308\n",
      "starting to reduce usage for unit 2897 with usage 1.08519 . Total units tried = 205\n",
      "try to drop unit 2897 from 5 th HD.  usage down to 1.0770277558746915\n",
      "try to drop unit 2897 from 10 th HD.  usage down to 1.0529729865329434\n",
      "all done trying to reduce usage of unit 2897 final usage = 1.02289 1272 sec elapsed 5 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00188 0.04302\n",
      "starting to reduce usage for unit 752 with usage 1.08505 . Total units tried = 206\n",
      "all done trying to reduce usage of unit 752 final usage = 1.02636 1278 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00188 0.04297\n",
      "starting to reduce usage for unit 9907 with usage 1.0849 . Total units tried = 207\n",
      "all done trying to reduce usage of unit 9907 final usage = 1.02828 1284 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00188 0.04293\n",
      "starting to reduce usage for unit 3441 with usage 1.08482 . Total units tried = 208\n",
      "all done trying to reduce usage of unit 3441 final usage = 1.01764 1291 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00187 0.04288\n",
      "starting to reduce usage for unit 2565 with usage 1.084 . Total units tried = 209\n",
      "try to drop unit 2565 from 9 th HD.  usage down to 1.0385418029724973\n",
      "all done trying to reduce usage of unit 2565 final usage = 1.02732 1298 sec elapsed 5 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00187 0.04283\n",
      "starting to reduce usage for unit 5697 with usage 1.0839 . Total units tried = 210\n",
      "all done trying to reduce usage of unit 5697 final usage = 1.02323 1304 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00187 0.04277\n",
      "starting to reduce usage for unit 9608 with usage 1.08356 . Total units tried = 211\n",
      "all done trying to reduce usage of unit 9608 final usage = 1.02363 1310 sec elapsed 4 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00187 0.04271\n",
      "starting to reduce usage for unit 9416 with usage 1.08333 . Total units tried = 212\n",
      "try to drop unit 9416 from 9 th HD.  usage down to 1.0688892395645222\n",
      "try to drop unit 9416 from 18 th HD.  usage down to 1.0591985319992165\n",
      "all done trying to reduce usage of unit 9416 final usage = 1.02557 1316 sec elapsed 5 0 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00186 0.04262\n",
      "starting to reduce usage for unit 1468 with usage 1.08332 . Total units tried = 213\n",
      "all done trying to reduce usage of unit 1468 final usage = 1.02617 1322 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00186 0.04259\n",
      "starting to reduce usage for unit 9573 with usage 1.08573 . Total units tried = 214\n",
      "all done trying to reduce usage of unit 9573 final usage = 1.02909 1328 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00186 0.04253\n",
      "starting to reduce usage for unit 1262 with usage 1.08311 . Total units tried = 215\n",
      "try to drop unit 1262 from 6 th HD.  usage down to 1.0329623046772596\n",
      "all done trying to reduce usage of unit 1262 final usage = 1.02182 1334 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00185 0.04248\n",
      "starting to reduce usage for unit 1255 with usage 1.09422 . Total units tried = 216\n",
      "try to drop unit 1255 from 7 th HD.  usage down to 1.0300341168501521\n",
      "all done trying to reduce usage of unit 1255 final usage = 1.01847 1341 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00185 0.04242\n",
      "starting to reduce usage for unit 4139 with usage 1.08283 . Total units tried = 217\n"
     ]
    },
    {
     "ename": "ZeroDivisionError",
     "evalue": "integer division or modulo by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[167], line 63\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m unitUse[OUU] \u001b[38;5;241m>\u001b[39m stopMaxUse \u001b[38;5;129;01mand\u001b[39;00m idxNo \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.5\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mlen\u001b[39m(ouuHDs): \u001b[38;5;66;03m#arbitrarily only examine 50% of HDs, biasing farthest ones\u001b[39;00m\n\u001b[0;32m     62\u001b[0m     idxNo \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 63\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43midxNo\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mouuHDs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m     64\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtry to drop unit\u001b[39m\u001b[38;5;124m\"\u001b[39m,OUU,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrom\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mabs\u001b[39m(idxNo),\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mth HD.  usage down to\u001b[39m\u001b[38;5;124m\"\u001b[39m,unitUse[OUU] )\n\u001b[0;32m     65\u001b[0m     t \u001b[38;5;241m=\u001b[39m ouuHDs[idx0[idxNo]]\n",
      "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  run second for most states except MA\n",
    "maxSD = 0.04 #0.06  #0.08  #0.04  #oops, did 0.06 first for 0.36 / 1.5 case\n",
    "stopMaxUse = 1.03 #1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = 0.05 #float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "87cdc52d-8ffe-449c-94d2-36c2e7e622f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.03\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 2550 units out of 10008 with usage above 1.03\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n",
      "this block reduces overuse, with exchanges up to 5959\n",
      "current avg and SD of unit usage are 1.00185 0.04242 . Now trying to reduce up to 2550 units' overusage\n",
      "starting to reduce usage for unit 4004 with usage 1.12282 . Total units tried = 1\n",
      "try to drop unit 4004 from 7 th HD.  usage down to 1.1228215930100736\n",
      "try to drop unit 4004 from 14 th HD.  usage down to 1.1228215930100736\n",
      "try to drop unit 4004 from 21 th HD.  usage down to 1.1228215930100736\n",
      "try to drop unit 4004 from 28 th HD.  usage down to 1.1228215930100736\n",
      "try to drop unit 4004 from 35 th HD.  usage down to 1.1228215930100736\n",
      "all done trying to reduce usage of unit 4004 final usage = 1.12282 0 sec elapsed 0 1 successful, failed patches, couldn't start= 35\n",
      "current avg and SD of usage are 1.00185 0.04242\n",
      "starting to reduce usage for unit 4139 with usage 1.08283 . Total units tried = 2\n",
      "try to drop unit 4139 from 2 th HD.  usage down to 1.0662547095423234\n",
      "all done trying to reduce usage of unit 4139 final usage = 1.06027 5 sec elapsed 2 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00184 0.04239\n",
      "starting to reduce usage for unit 8791 with usage 1.08271 . Total units tried = 3\n",
      "all done trying to reduce usage of unit 8791 final usage = 1.02979 11 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00184 0.04236\n",
      "starting to reduce usage for unit 9778 with usage 1.08253 . Total units tried = 4\n",
      "all done trying to reduce usage of unit 9778 final usage = 1.0268 17 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00184 0.04228\n",
      "starting to reduce usage for unit 9130 with usage 1.08215 . Total units tried = 5\n",
      "all done trying to reduce usage of unit 9130 final usage = 1.02649 22 sec elapsed 4 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00184 0.04223\n",
      "starting to reduce usage for unit 8646 with usage 1.08184 . Total units tried = 6\n",
      "all done trying to reduce usage of unit 8646 final usage = 1.01753 28 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00183 0.04216\n",
      "starting to reduce usage for unit 1952 with usage 1.08167 . Total units tried = 7\n",
      "all done trying to reduce usage of unit 1952 final usage = 1.02414 34 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00183 0.04213\n",
      "starting to reduce usage for unit 6226 with usage 1.08158 . Total units tried = 8\n",
      "try to drop unit 6226 from 6 th HD.  usage down to 1.0344389839252977\n",
      "all done trying to reduce usage of unit 6226 final usage = 1.02401 40 sec elapsed 4 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00183 0.04207\n",
      "starting to reduce usage for unit 6498 with usage 1.08137 . Total units tried = 9\n",
      "try to drop unit 6498 from 7 th HD.  usage down to 1.0547768844780183\n",
      "all done trying to reduce usage of unit 6498 final usage = 1.02133 46 sec elapsed 7 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00183 0.04202\n",
      "starting to reduce usage for unit 9479 with usage 1.08134 . Total units tried = 10\n",
      "all done trying to reduce usage of unit 9479 final usage = 1.01689 53 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00183 0.04197\n",
      "starting to reduce usage for unit 7376 with usage 1.0811 . Total units tried = 11\n",
      "try to drop unit 7376 from 7 th HD.  usage down to 1.0555320045480334\n",
      "all done trying to reduce usage of unit 7376 final usage = 1.02669 60 sec elapsed 4 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00182 0.04194\n",
      "starting to reduce usage for unit 2242 with usage 1.08061 . Total units tried = 12\n",
      "try to drop unit 2242 from 5 th HD.  usage down to 1.0516137704069137\n",
      "all done trying to reduce usage of unit 2242 final usage = 1.02806 66 sec elapsed 5 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00182 0.04187\n",
      "starting to reduce usage for unit 5627 with usage 1.08033 . Total units tried = 13\n",
      "try to drop unit 5627 from 4 th HD.  usage down to 1.0803335037367983\n",
      "try to drop unit 5627 from 8 th HD.  usage down to 1.0803335037367983\n",
      "try to drop unit 5627 from 12 th HD.  usage down to 1.0803335037367983\n",
      "try to drop unit 5627 from 16 th HD.  usage down to 1.0803335037367983\n",
      "try to drop unit 5627 from 20 th HD.  usage down to 1.0803335037367983\n",
      "all done trying to reduce usage of unit 5627 final usage = 1.08033 72 sec elapsed 0 0 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 1.00182 0.04187\n",
      "starting to reduce usage for unit 5626 with usage 1.08033 . Total units tried = 14\n",
      "try to drop unit 5626 from 4 th HD.  usage down to 1.0803335037367983\n",
      "try to drop unit 5626 from 8 th HD.  usage down to 1.0803335037367983\n",
      "try to drop unit 5626 from 12 th HD.  usage down to 1.0803335037367983\n",
      "try to drop unit 5626 from 16 th HD.  usage down to 1.0803335037367983\n",
      "try to drop unit 5626 from 20 th HD.  usage down to 1.0803335037367983\n",
      "all done trying to reduce usage of unit 5626 final usage = 1.08033 78 sec elapsed 0 0 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 1.00182 0.04187\n",
      "starting to reduce usage for unit 1430 with usage 1.08032 . Total units tried = 15\n",
      "try to drop unit 1430 from 6 th HD.  usage down to 1.0336922540783695\n",
      "all done trying to reduce usage of unit 1430 final usage = 1.02466 84 sec elapsed 4 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00182 0.04183\n",
      "starting to reduce usage for unit 4005 with usage 1.08029 . Total units tried = 16\n",
      "try to drop unit 4005 from 6 th HD.  usage down to 1.035739468490343\n",
      "all done trying to reduce usage of unit 4005 final usage = 1.02445 91 sec elapsed 4 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00182 0.0418\n",
      "starting to reduce usage for unit 4012 with usage 1.08016 . Total units tried = 17\n",
      "try to drop unit 4012 from 5 th HD.  usage down to 1.0801573090537586\n",
      "try to drop unit 4012 from 10 th HD.  usage down to 1.0801573090537586\n",
      "try to drop unit 4012 from 15 th HD.  usage down to 1.06994640766244\n",
      "try to drop unit 4012 from 20 th HD.  usage down to 1.06994640766244\n",
      "try to drop unit 4012 from 25 th HD.  usage down to 1.06994640766244\n",
      "all done trying to reduce usage of unit 4012 final usage = 1.06995 97 sec elapsed 1 0 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 1.00182 0.04179\n",
      "starting to reduce usage for unit 943 with usage 1.07985 . Total units tried = 18\n",
      "all done trying to reduce usage of unit 943 final usage = 1.01941 104 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00182 0.04171\n",
      "starting to reduce usage for unit 4569 with usage 1.07983 . Total units tried = 19\n",
      "try to drop unit 4569 from 7 th HD.  usage down to 1.0396493124085164\n",
      "all done trying to reduce usage of unit 4569 final usage = 1.0274 110 sec elapsed 4 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00181 0.04167\n",
      "starting to reduce usage for unit 158 with usage 1.0798 . Total units tried = 20\n",
      "all done trying to reduce usage of unit 158 final usage = 1.02599 116 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00181 0.04161\n",
      "starting to reduce usage for unit 4045 with usage 1.0797 . Total units tried = 21\n",
      "all done trying to reduce usage of unit 4045 final usage = 1.02252 122 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00181 0.04156\n",
      "starting to reduce usage for unit 4796 with usage 1.07928 . Total units tried = 22\n",
      "all done trying to reduce usage of unit 4796 final usage = 1.01854 128 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00181 0.04152\n",
      "starting to reduce usage for unit 149 with usage 1.07908 . Total units tried = 23\n",
      "try to drop unit 149 from 7 th HD.  usage down to 1.0694765551744925\n",
      "all done trying to reduce usage of unit 149 final usage = 1.02158 134 sec elapsed 5 4 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00181 0.04147\n",
      "starting to reduce usage for unit 3730 with usage 1.07904 . Total units tried = 24\n",
      "try to drop unit 3730 from 7 th HD.  usage down to 1.0365029787833897\n",
      "all done trying to reduce usage of unit 3730 final usage = 1.01813 140 sec elapsed 5 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0018 0.04142\n",
      "starting to reduce usage for unit 2625 with usage 1.07893 . Total units tried = 25\n",
      "all done trying to reduce usage of unit 2625 final usage = 1.02585 146 sec elapsed 7 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.0018 0.04136\n",
      "starting to reduce usage for unit 8618 with usage 1.07893 . Total units tried = 26\n",
      "all done trying to reduce usage of unit 8618 final usage = 1.02736 152 sec elapsed 3 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.0018 0.04129\n",
      "starting to reduce usage for unit 78 with usage 1.07887 . Total units tried = 27\n",
      "try to drop unit 78 from 8 th HD.  usage down to 1.05532224897315\n",
      "try to drop unit 78 from 16 th HD.  usage down to 1.045715443637847\n",
      "try to drop unit 78 from 24 th HD.  usage down to 1.045715443637847\n",
      "try to drop unit 78 from 32 th HD.  usage down to 1.045715443637847\n",
      "all done trying to reduce usage of unit 78 final usage = 1.01416 158 sec elapsed 7 4 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.00179 0.04123\n",
      "starting to reduce usage for unit 941 with usage 1.07876 . Total units tried = 28\n",
      "all done trying to reduce usage of unit 941 final usage = 1.02551 164 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00179 0.04115\n",
      "starting to reduce usage for unit 377 with usage 1.07846 . Total units tried = 29\n",
      "try to drop unit 377 from 9 th HD.  usage down to 1.078462484007895\n",
      "try to drop unit 377 from 18 th HD.  usage down to 1.078462484007895\n",
      "try to drop unit 377 from 27 th HD.  usage down to 1.078462484007895\n",
      "try to drop unit 377 from 36 th HD.  usage down to 1.078462484007895\n",
      "try to drop unit 377 from 45 th HD.  usage down to 1.078462484007895\n",
      "all done trying to reduce usage of unit 377 final usage = 1.07846 171 sec elapsed 0 0 successful, failed patches, couldn't start= 48\n",
      "current avg and SD of usage are 1.00179 0.04115\n",
      "starting to reduce usage for unit 1713 with usage 1.07835 . Total units tried = 30\n",
      "try to drop unit 1713 from 8 th HD.  usage down to 1.0378454144634968\n",
      "all done trying to reduce usage of unit 1713 final usage = 1.02722 177 sec elapsed 4 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00179 0.0411\n",
      "starting to reduce usage for unit 7400 with usage 1.07829 . Total units tried = 31\n",
      "all done trying to reduce usage of unit 7400 final usage = 1.02392 183 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00178 0.04105\n",
      "starting to reduce usage for unit 6406 with usage 1.07817 . Total units tried = 32\n",
      "try to drop unit 6406 from 10 th HD.  usage down to 1.043961887030687\n",
      "all done trying to reduce usage of unit 6406 final usage = 1.02676 189 sec elapsed 5 5 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00178 0.04099\n",
      "starting to reduce usage for unit 2197 with usage 1.07814 . Total units tried = 33\n",
      "try to drop unit 2197 from 6 th HD.  usage down to 1.030940260934245\n",
      "all done trying to reduce usage of unit 2197 final usage = 1.02336 196 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00178 0.04095\n",
      "starting to reduce usage for unit 8446 with usage 1.07748 . Total units tried = 34\n",
      "all done trying to reduce usage of unit 8446 final usage = 1.02398 202 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00178 0.04092\n",
      "starting to reduce usage for unit 4938 with usage 1.07747 . Total units tried = 35\n",
      "all done trying to reduce usage of unit 4938 final usage = 1.02258 208 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04088\n",
      "starting to reduce usage for unit 9480 with usage 1.07744 . Total units tried = 36\n",
      "all done trying to reduce usage of unit 9480 final usage = 1.02857 214 sec elapsed 4 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00177 0.04084\n",
      "starting to reduce usage for unit 325 with usage 1.07717 . Total units tried = 37\n",
      "try to drop unit 325 from 10 th HD.  usage down to 1.077170389665854\n",
      "try to drop unit 325 from 20 th HD.  usage down to 1.077170389665854\n",
      "try to drop unit 325 from 30 th HD.  usage down to 1.077170389665854\n",
      "try to drop unit 325 from 40 th HD.  usage down to 1.077170389665854\n",
      "try to drop unit 325 from 50 th HD.  usage down to 1.0720775243046934\n",
      "all done trying to reduce usage of unit 325 final usage = 1.06541 221 sec elapsed 2 2 successful, failed patches, couldn't start= 49\n",
      "current avg and SD of usage are 1.00177 0.04082\n",
      "starting to reduce usage for unit 6156 with usage 1.07689 . Total units tried = 38\n",
      "all done trying to reduce usage of unit 6156 final usage = 1.02347 228 sec elapsed 3 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00177 0.04078\n",
      "starting to reduce usage for unit 6286 with usage 1.07688 . Total units tried = 39\n",
      "try to drop unit 6286 from 5 th HD.  usage down to 1.0768767318606756\n",
      "try to drop unit 6286 from 10 th HD.  usage down to 1.0445156417486712\n",
      "all done trying to reduce usage of unit 6286 final usage = 1.01735 234 sec elapsed 5 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00177 0.04074\n",
      "starting to reduce usage for unit 4860 with usage 1.07687 . Total units tried = 40\n",
      "try to drop unit 4860 from 9 th HD.  usage down to 1.0768683416376634\n",
      "try to drop unit 4860 from 18 th HD.  usage down to 1.0768683416376634\n",
      "try to drop unit 4860 from 27 th HD.  usage down to 1.0515214779538753\n",
      "all done trying to reduce usage of unit 4860 final usage = 1.01606 239 sec elapsed 4 0 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 1.00177 0.04069\n",
      "starting to reduce usage for unit 208 with usage 1.07678 . Total units tried = 41\n",
      "all done trying to reduce usage of unit 208 final usage = 1.02456 246 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04061\n",
      "starting to reduce usage for unit 1181 with usage 1.08301 . Total units tried = 42\n",
      "all done trying to reduce usage of unit 1181 final usage = 1.02614 252 sec elapsed 4 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00177 0.04056\n",
      "starting to reduce usage for unit 8907 with usage 1.07673 . Total units tried = 43\n",
      "try to drop unit 8907 from 7 th HD.  usage down to 1.0607087721391577\n",
      "all done trying to reduce usage of unit 8907 final usage = 1.02293 258 sec elapsed 4 3 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00177 0.04051\n",
      "starting to reduce usage for unit 9152 with usage 1.07657 . Total units tried = 44\n",
      "all done trying to reduce usage of unit 9152 final usage = 1.01945 264 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00177 0.04045\n",
      "starting to reduce usage for unit 6048 with usage 1.0765 . Total units tried = 45\n",
      "try to drop unit 6048 from 5 th HD.  usage down to 1.062638523429269\n",
      "all done trying to reduce usage of unit 6048 final usage = 1.02272 269 sec elapsed 4 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00176 0.0404\n",
      "starting to reduce usage for unit 3613 with usage 1.07647 . Total units tried = 46\n",
      "try to drop unit 3613 from 4 th HD.  usage down to 1.0388270705544822\n",
      "all done trying to reduce usage of unit 3613 final usage = 1.02774 275 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00176 0.04037\n",
      "starting to reduce usage for unit 8639 with usage 1.0761 . Total units tried = 47\n",
      "try to drop unit 8639 from 3 th HD.  usage down to 1.0760964411216742\n",
      "try to drop unit 8639 from 6 th HD.  usage down to 1.041268625447595\n",
      "all done trying to reduce usage of unit 8639 final usage = 1.02871 281 sec elapsed 4 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00176 0.04032\n",
      "starting to reduce usage for unit 9558 with usage 1.07553 . Total units tried = 48\n",
      "all done trying to reduce usage of unit 9558 final usage = 1.02144 287 sec elapsed 3 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00176 0.04025\n",
      "starting to reduce usage for unit 8963 with usage 1.07535 . Total units tried = 49\n",
      "all done trying to reduce usage of unit 8963 final usage = 1.0266 293 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00175 0.04022\n",
      "starting to reduce usage for unit 401 with usage 1.07532 . Total units tried = 50\n",
      "try to drop unit 401 from 3 th HD.  usage down to 1.064039690670437\n",
      "try to drop unit 401 from 6 th HD.  usage down to 1.064039690670437\n",
      "try to drop unit 401 from 9 th HD.  usage down to 1.0544077146661333\n",
      "all done trying to reduce usage of unit 401 final usage = 1.02253 299 sec elapsed 6 4 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00175 0.04016\n",
      "starting to reduce usage for unit 1860 with usage 1.07529 . Total units tried = 51\n",
      "all done trying to reduce usage of unit 1860 final usage = 1.02134 305 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00175 0.04013\n",
      "starting to reduce usage for unit 8935 with usage 1.07503 . Total units tried = 52\n",
      "all done trying to reduce usage of unit 8935 final usage = 1.02785 311 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00174 0.04008\n",
      "starting to reduce usage for unit 3000 with usage 1.0897 . Total units tried = 53\n",
      "try to drop unit 3000 from 6 th HD.  usage down to 1.0416042343675826\n",
      "all done trying to reduce usage of unit 3000 final usage = 1.02462 317 sec elapsed 5 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00175 0.03998\n"
     ]
    }
   ],
   "source": [
    "#repeat above with error fix\n",
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  run second for most states except MA\n",
    "maxSD = 0.04 #0.06  #0.08  #0.04  #oops, did 0.06 first for 0.36 / 1.5 case\n",
    "stopMaxUse = 1.03 #1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = 0.05 #float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % max(2,int(0.1*len(ouuHDs))) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "4bf299e8-0054-4151-8c70-d29cb67681df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.009068992040983612 0.9174600167163308\n"
     ]
    }
   ],
   "source": [
    "print(maxGap/aDP, maxGap/np.average(unitPop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "underused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"overused units\")\n",
    "maxPlot = 1.06\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"underused units\")\n",
    "minPlot = 0.94\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "0d4f1372-6044-4803-91d9-de4b95a40083",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00284 1.00175 and their SDs are 0.093 0.03998\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1600 out of 8941\n",
      "working on HD 2400 out of 8941\n",
      "working on HD 3200 out of 8941\n",
      "working on HD 4000 out of 8941\n",
      "working on HD 4800 out of 8941\n",
      "working on HD 5600 out of 8941\n",
      "working on HD 6400 out of 8941\n",
      "working on HD 7200 out of 8941\n",
      "working on HD 8000 out of 8941\n",
      "working on HD 8800 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "ac9cd133-94f0-494b-ac74-0c584482d97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"legisl60_1p5Patched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "26b87c95-101f-4f8f-bb73-feb1827ac6b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    }
   ],
   "source": [
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))\n",
    "if noFrag == 1:\n",
    "    nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "ec102553-e4f3-4010-86bc-725a01b95788",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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hvMBp0fz3gwADAGFAeIEV+z1HLb8m2v9+EGAAIMQIL7Dq50+av2VAfm5qTPz9IMAAQAjZDS/ZTeNj4sMJgcqqzN98MatlhnONRBAm8QJAiNgNL49df46u6Z0bpG4QrQb3iI1NDAkwAOAwb61PZ05YZGsMNqiLbVbuHHBRp1aO9RFJCDAA4KD7X9uo2SU7bY3BJaPYVlZp/vJRIylmgi4BBgAc0q14sQ5662yNQXjBoBkrTdfWOthHpCHAAIADWGmEYPnmcI3p2gQH+4g0rEICgCAjvCBc+nXICHcLIUOAAYAgIrwg2LqelmS69slhfRzsJLIQYAAgSAgvcMKAHuaWzmelJappcuzMDCHAAEAQ2A0vcSK8oH77D5ibA1N4dmuHO4ksBBgAsMlueLm0U3NtJ7zgBHLSk4NaFy1i51wTAARZnc9Qh3sW2hpjy8QrlZIYS2tHYNX2rw8GtS5aEGAAoAFeW79Hd8xZb2sMLhnBjDU7vg1qXbQgwACARQOnv6PNX3lsjUF4gVk1deZuJGC2LloQYADAgo7jF6jWsDcG4QVW9GiTri+/PfXtBHq0SQ9BN5GDAAMAJrFMGuEwf0O5qbrre7V1uJPIwiokADCB8IJw2FNRLZsn/KIWAQYAToHwgnAZMGOF6dpX1+9xsJPIQ4ABgJMgvCCcDhwxfzfzQzbvfO42BBgAOAG74aVzZjLhBbbEx5mvPb99c+caiUBM4gWAetgNLzNuOFdX92wTpG4Qq+osTIAZflGec41EIAIMAPxAtbdOXYoX2xrj88kDlWDln85APTZ/aW2vocRGsXVRhQADAP/nxufW6O1t+22NwSUjBMtVT7xjujYW47LluLZnzx795je/UcuWLZWSkqLu3bvrgw8+8D9vGIaKi4vVunVrpaSkqKCgQJ9++mnAGBUVFRo6dKjS0tKUkZGhESNG6ODBwHs4fPzxx7r44ouVnJys3NxcTZs2rYFvEQBOrfOERYQXRBQr++r+7ZqzHesjUlkKMN9++6369u2rxo0ba9GiRdq8ebP+/ve/q3nz7ycOTZs2TTNmzNDTTz+t1atXq0mTJiosLNSRI9/vIjh06FBt2rRJS5cu1fz587Vy5Urdcsst/uc9Ho/69++vdu3aqbS0VA8//LD+8pe/6JlnngnCWwaAQO3HLdCRWnvbsBNeEExVh2ss1V9zQTuHOolccYZhmJ4iNG7cOK1atUrvvFP/aS3DMJSTk6M//elP+vOf/yxJqqqqUlZWlmbNmqUhQ4Zoy5Yt6tq1q9auXavevXtLkhYvXqyBAwfqyy+/VE5OjmbOnKl7771XZWVlSkxM9H/vV199VZ988ompXj0ej9LT01VVVaW0tDSzbxFAjGGZNCJRt/sW6mCN+Rm80fR30Oznt6UzMK+//rp69+6tX/ziF8rMzFSPHj307LPP+p/fvn27ysrKVFBQ4H8sPT1dffr0UUlJiSSppKREGRkZ/vAiSQUFBYqPj9fq1av9NZdccok/vEhSYWGhtm7dqm+/rf9um0ePHpXH4wn4AoCTIbwgElV76yyFl0axOAFGFgPMF198oZkzZ6pTp05asmSJRo4cqdtvv12zZ8+WJJWVlUmSsrKyAl6XlZXlf66srEyZmZkBzzdq1EgtWrQIqKlvjB9+jx+bMmWK0tPT/V+5ublW3hqAGEN4QaSavHCzpfp2LZId6iSyWQowPp9PPXv21OTJk9WjRw/dcsstuvnmm/X000871Z9p48ePV1VVlf9r9+7d4W4JQISyG14SRXiBcz4tP3jqoh+YO/JihzqJbJYCTOvWrdW1a9eAx7p06aJdu3ZJkrKzsyVJ5eWBd84sLy/3P5edna19+/YFPF9bW6uKioqAmvrG+OH3+LGkpCSlpaUFfAHAj9kNL8Py22gb4QUO2ry3ylJ9i6aJpy6KQpYCTN++fbV169aAx7Zt26Z27b6b/ZyXl6fs7GwtW7bM/7zH49Hq1auVn58vScrPz1dlZaVKS0v9NcuXL5fP51OfPn38NStXrlRNzfezsJcuXaqzzjorYMUTAJhV5zNsh5dtkwbowZ+fG6SOgPp5jpq/p9H7465wsJPIZinAjBkzRu+//74mT56szz77TC+99JKeeeYZFRUVSZLi4uI0evRoTZo0Sa+//ro2bNig3/72t8rJydE111wj6bszNldeeaVuvvlmrVmzRqtWrdKoUaM0ZMgQ5eTkSJJ+/etfKzExUSNGjNCmTZv08ssva/r06brzzjuD++4BxIR56/aowz0LbY2xY+qgmNvpFKF3lsWQnZ0Rm/NfJIs78Z5//vmaN2+exo8fr4kTJyovL0+PPfaYhg4d6q+56667dOjQId1yyy2qrKxUv379tHjxYiUnf3+QX3zxRY0aNUpXXHGF4uPjNXjwYM2YMcP/fHp6ut58800VFRWpV69eatWqlYqLiwP2igEAM/pNXa4vK6ttjcF8F4RC1eEaHQ13Ey5iaR8YN2EfGAB54xbI7i84wgtCZfBTq1S6q9LSa6Lx76fZz2/uhQQgKrFMGm6zx+KZwg4tY/fykdSAeyEBQKQjvMCNyjzWLiDF6vLpYwgwAKIK4QVutOazCkv1pzVNjNnl08cQYABEDbvhJU6EF4THDf8ssVS/dsJPHerEPQgwAKKC3fDSJStF2wkvCINqr/l9X/A9JvECcLU6n2F7j5cZN5yrq3u2CVJHgDWT5m+yVN82LcGhTtyFAAPAtV5bv0d3zFlva4zPJw9UQnyM3s4XEeG9L76xVP/q7Zc71Im7EGAAuNLA6e9o81ceW2Mw3wWRwEp8bhQfu/c++jECDADX6Th+gWpt7lBHeEGk2FNx2HRt6YT+DnbiLgQYAK7CMmlEm6M+87XpqY2da8RlWIUEwDUIL4g2VYdrTNfygR2I4wHAFQgviEY3zVpjutbCiZqYQIABEPEIL4hWX+w/ZLqWD+xAHA8AEc1ueElrTHhB5KqsNn8JqX2LJAc7cR8CDICIZTe8DL8oVx8/SHhBZDp4pNZS/f/cdolDnbgTq5AARBxvrU9nTlhka4xtkwYosRH/RkPkGj1nnaV69n8JRIABEFEeeH2Tnn9vh60xuGQEN1i3uzLcLbgaAQZAxOg1cam+Oey1NQbhBW5Rccj8/JcWKdz/6McIMAAiAiuNEEvWfFZhqX7hHZc604iLcYEYQNgRXhBrbvhniaX67IxkhzpxLwIMgLAivAAnx8Wj+hFgAIQN4QWxyFtrbU/dJ35xnjONuBwBBkBYEF4Qq5579wtL9f175DjUibsRYACEnN3w0jkzmfAC15r34R7TtbkZyUqIj3OwG/diFRKAkKnzGepwz0JbY8y44Vxd3bNNkDoCQu+ohUtIE3/WzcFO3I0AAyAk5q3bozGvrLc1xueTB/KvUbheh1ap2vHNYVO1l3TJdLgb9yLAAHBcv6nL9WVlta0xuGSEaHFF1ywt2/r1Ket+fUEugf0kCDAAHJU3boEMm2MQXhBN3vpkv6m6/QeOOtyJuxFgADiGlUbA8apr6oJaF6tYhQTAEYQXoH55rZoEtS5WEWAABB3hBTix2y8/M6h1sYoAAyCoCC/AyY2duz6odbGKAAMgaOyGl9bNEggviHqrvvgmqHWxikm8AILCbnh57PpzdE3v3CB1A0Sumjpz6/LM1sUqAgwAW7y1Pp05YZGtMdigDrGizmc+lDTiR+KkCDAAGuz+1zZqdslOW2NwyQixZM32CtO1XbJSHezE/QgwABqkW/FiHfTa26eC8IJYU+Y5Yrr27LYtHOzE/QgwACxjpRHQMBUHze+u2+m0Zg524n6sQgJgCeEFaLi5a3eYqouTNCy/vZOtuB4BBoBphBeg4eav36NP9pm7qWly43glNuIj+mQ4OgBMIbwADVfnM/THOetN11fX+JxrJkoQYACckt3wEifCC2Lbmu0Vtu/KjkAEGAAnZTe8XNqpubYTXhDj9h0wv/oI5rAKCUC9grFB3ZaJVyolMSFIHQHuldks2VL9q7f2daiT6EGAAXCcB17fpOff22FrDC4ZAd+7IM/ani7ntc9wppEoQoABEKDXxKX65rDX1hiEF+B4qYkJOmxi80d+fswhwADwY6UR4Iz3v/jGVHh58fd9QtBNdGASLwBJhBfASSWffxPUOhBgAIjwAjjP7CJqFlubRYABYhzhBXBen7yWQa0DAQaIaYQXIDQOVNcEtQ4EGCBm2Q0vnTOTCS+ASRNe2xjUOrAKCYhJdsPLjBvO1dU92wSpGyD6HTp66hVIVupAgAFiSrW3Tl2KF9sa4/PJA5UQHxekjoDY0DQpXkcPn/oGjU2TuDBiFkcKiBHDn1tjO7zsmDqI8AI0wOifdgpqHTgDA8SETvcsVI3P3vJM5rsADdcxMz2odSDAAFGPlUZA+PVq11zxcdLJ/h0RH/ddHczhEhIQxQgvQGQo3fntScOL9F24Kd35bWgaigIEGCBKEV6AyPFVZXVQ60CAAaIS4QWILKU7K4JaBwIMEHXshpekOMILEGyflHmCWgcCDBBV7IaXYflttHUK4QUItrg4cx+3ZuvAKiQgKnhrfTpzwiJbY2ybNECJjfjlCTjB7OTcK7pkOtxJ9CDAAC53/2sbNbtkp60xuGQEOKes8ojM7sLULiPV0V6iia1/bk2dOlVxcXEaPXq0/7EjR46oqKhILVu2VNOmTTV48GCVl5cHvG7Xrl0aNGiQUlNTlZmZqbFjx6q2tjag5u2331bPnj2VlJSkjh07atasWXZaBaJSt+LFhBcgwl31+ErTtRMXbnawk+jS4ACzdu1a/eMf/9A555wT8PiYMWP0xhtvaO7cuVqxYoX27t2r6667zv98XV2dBg0aJK/Xq/fee0+zZ8/WrFmzVFxc7K/Zvn27Bg0apMsuu0zr16/X6NGj9fvf/15LlixpaLtA1Gk/boEOeu3d+I3wAjjPc6T21EX/54CF2ljXoABz8OBBDR06VM8++6yaN/9+18Cqqir961//0iOPPKLLL79cvXr10vPPP6/33ntP77//viTpzTff1ObNm/XCCy/ovPPO04ABA/Tggw/qySeflNfrlSQ9/fTTysvL09///nd16dJFo0aN0vXXX69HH300CG8ZcD+WSQPukZZsfrZGTnqSg51ElwYFmKKiIg0aNEgFBQUBj5eWlqqmpibg8c6dO6tt27YqKSmRJJWUlKh79+7Kysry1xQWFsrj8WjTpk3+mh+PXVhY6B+jPkePHpXH4wn4AqIR4QVwl/l/vMR07ct/6OtgJ9HF8iTeOXPmaN26dVq7du1xz5WVlSkxMVEZGRkBj2dlZamsrMxf88Pwcuz5Y8+drMbj8ai6ulopKSnHfe8pU6bogQcesPp2AFchvADuk52RrJTG8aqu8Z207rSmiWrRNDFEXbmfpTMwu3fv1h133KEXX3xRycnJTvXUIOPHj1dVVZX/a/fu3eFuCQgqu+ElToQXIFy2PDhAKY1P/JF7WtNErZ3w0xB25H6WzsCUlpZq37596tmzp/+xuro6rVy5Uk888YSWLFkir9erysrKgLMw5eXlys7OliRlZ2drzZo1AeMeW6X0w5ofr1wqLy9XWlpavWdfJCkpKUlJSVw7RHSyG14u7dRcs0ZcFKRuADTElgcHqKzyiPr/fZk8Nd89dkbLVP3PyL6ceWkASwHmiiuu0IYNGwIeu+mmm9S5c2fdfffdys3NVePGjbVs2TINHjxYkrR161bt2rVL+fn5kqT8/Hw99NBD2rdvnzIzv9uwZ+nSpUpLS1PXrl39NQsXLgz4PkuXLvWPAcSKOp+hDvcsPHXhSWyZeKVSEhOC1BEAO7IzkvXxg5wJDQZLAaZZs2bq1q1bwGNNmjRRy5Yt/Y+PGDFCd955p1q0aKG0tDT98Y9/VH5+vi688EJJUv/+/dW1a1cNGzZM06ZNU1lZmSZMmKCioiL/GZRbb71VTzzxhO666y797ne/0/Lly/XKK69owQL71/8Bt3ht/R7dMWe9rTG4ZARElrLKI7rq8ZXyHKlVWnIjzf/jJcrOiKwpGW4R9J14H330UcXHx2vw4ME6evSoCgsL9dRTT/mfT0hI0Pz58zVy5Ejl5+erSZMmGj58uCZOnOivycvL04IFCzRmzBhNnz5dbdq00T//+U8VFhYGu10gIg2c/o42f2VvJR3hBYgsXe5bFDCR9+tDNbpw6jKlNI7XlgcHhLEzd4ozDMPsDseu4vF4lJ6erqqqKqWlpYW7HcC0juMXqNbmTyXhBYgsPw4vP0aI+Z7Zz2/u3AZEkPbjCC9AtCmrPHLKJdTVNT6VVR4JUUfRgQADRAj2eAGik9l7IVm5ZxIIMEBEILwA0avK5P2NzNbhOwQYIMwIL0B0q6kzd124cXycw51EFwIMEEZ2w0taY8ILEMkqDnpN19bVnnyeDAIRYIAwsRtehl+Uy4ZYQIT75TPvma6NS+Aj2Yqg7wMD4OSqvXXqUrzY1hjbJg1QYiN+2QGRbq+FlUVtMrgdjhUEGCCEhj+3Riu27bc1BpeMAPfw1taZrn3l1n4OdhJ9CDBAiHS6Z6FqfPY2eSG8AO5yiu1fAnBDR2sIMEAIsNIIAIKLi+iAwwgvQOwyuzA6mRvGW0aAARxEeAFil7fWJ7MXja/tfbqjvUQjAgzgEMILENumL91munbr3oMOdhKdCDCAA+yGl6Q4wgvgdjNXfG669qDX/GolfIcAAwSZ3fAyLL+Ntk4hvABuZ2Vf3XYtUh3rI1qxCgkIkjqfoQ73LLQ1BhvUAdHBa/G2AI/+sodDnUQvflMCQTBv3R7b4WXH1EGEFyBKzH5vh6X6psmcT7CKIwbY1G/qcn1ZWW1rDOa7ANHl/e3fmK7lHtQNQ4ABbMgbt8D0MskTIbwA0WftF+YDTPMUNoFpCAIM0EAskwZQH2+tT56j5lcVLbzjUueaiWJccAcagPAC4ET+u2SHpfrsjGRnGolyBBjAIsILgJPZWXE43C3EBAIMYAHhBcCpsKdLaBBgAJPshpfWzRIIL0AM+HWfdqZrX721r4OdRDcm8QKnEIwN6h67/hxd0zs3SB0BiGRrLCyhPq99hnONRDkCDHASr63fozvmrLc1xueTByohnp0egFjxzEpz90Dq26GFw51ENwIMcAKDpr+jTV95bI3BJSMg9ny2z9ydpc3WoX4EGKAeZ927UEfr7G1RR3gBYlPlIW9Q61A/AgzwI6w0AmBHQoJM3Yo6gQ14bWEVEvADhBcAdp2VlRbUOtSPAAP8H8ILgGD41419glqH+hFgABFeAATP1vIDQa1D/QgwiHl2w0taY8ILgO+NnfuhqbqvKqsd7iS6EWAQ0+yGl+EX5erjBwkvAL5z8Eitvqw8aqr23U/3O9xNdCPAICZ5a322w8u2SQP0wNXnBKkjANHg5tlrTde+/vFeBzuJfiyjRsy5/7WNml2y09YYXDICUJ+S7RWma+tMLLXGiRFgEFO6FS/RQW+trTEILwCCoUkitxixgwCDmMFKIwCRZMnoy8LdgqsxBwYxgfACwGm7vj5sqf70FikOdRIbCDCIeoQXAKFQOH2F6drT05Mc7CQ2EGAQ1QgvAEKlusb8rNzmTQgwdhFgELUILwAi1Z+uODPcLbgeAQZRyW546ZyZTHgBYNpPHrL2O+eSLpkOdRI7WIWEqGM3vMy44Vxd3bNNkLoBEO0OHqnVTgu3NUqMlxLiWUJtFwEGUaPaW6cuxYttjfH55IH8YgFgye3/Xmep/uc9cxzqJLYQYBAVhj+3Riu22buvCJeMADRE6a5vLdVP5BYkQUGAget1umehanyGrTEILwAa6uBRa7t7pyQmONRJbCHAwNVYaQQg3HwW7mm06q7LnWskxrAKCa5FeAEQCZo0Nj9vjt13g4cAA1civACIFAdrzF3C5sJRcBFg4DqEFwCRos7C/DsLV5pgAgEGrmI3vLRulkB4ARA073/xjenapMZ85AYTk3jhGnbDy2PXn6NreucGqRsAkN751Pz2Ddf3bu1gJ7GHAIOI56316cwJi2yNwQZ1AJzw7qdfm64d0JUdvoOJAIOIdv9rGzW7ZKetMbhkBMAphmFuDkx8nHRhh5YOdxNbCDCIWN2KF+ugt87WGIQXAE5q1TTRVF3n7KacBQ4yAgwiEiuNALjBik/NTeKtPOR1uJPYw5RoRBzCCwA3KKs8Yrp2r4cAE2wEGEQUwgsAt7jq8ZXhbiGmEWAQMQgvANzk28M14W4hphFgEBHshpc4EV4AhFYjC5Ny26Uz5TTYCDAIO7vh5dJOzbWd8AIgxLKamP8InffHyxzsJDYRCRE2dT5DHe5ZaGuMLROvVEoit0gDEHpH6uIlnXqrh/g4qYXJ5dYwjwCDsJi3bo/GvLLe1hhcMgIQLnU+Q/sOmZsD05j9Xxxh6RLSlClTdP7556tZs2bKzMzUNddco61btwbUHDlyREVFRWrZsqWaNm2qwYMHq7y8PKBm165dGjRokFJTU5WZmamxY8eqtrY2oObtt99Wz549lZSUpI4dO2rWrFkNe4eIOP2mLie8AHC1eaV7TNcerTN/x2qYZynArFixQkVFRXr//fe1dOlS1dTUqH///jp06JC/ZsyYMXrjjTc0d+5crVixQnv37tV1113nf76urk6DBg2S1+vVe++9p9mzZ2vWrFkqLi7212zfvl2DBg3SZZddpvXr12v06NH6/e9/ryVLlgThLSOczhi3QF9WVtsag/ACINz+/L8fma5tnMAZGCfEGWZv5FCP/fv3KzMzUytWrNAll1yiqqoqnXbaaXrppZd0/fXXS5I++eQTdenSRSUlJbrwwgu1aNEiXXXVVdq7d6+ysrIkSU8//bTuvvtu7d+/X4mJibr77ru1YMECbdy40f+9hgwZosrKSi1evNhUbx6PR+np6aqqqlJaWlpD3yKCiGXSAKJBxUGvek5aarr+uh5ZeuSXvR3sKLqY/fy2tQqpqqpKktSiRQtJUmlpqWpqalRQUOCv6dy5s9q2bauSkhJJUklJibp37+4PL5JUWFgoj8ejTZs2+Wt+OMaxmmNj1Ofo0aPyeDwBX4gchBcA0WLAY29bqn/o2h7ONBLjGhxgfD6fRo8erb59+6pbt26SpLKyMiUmJiojIyOgNisrS2VlZf6aH4aXY88fe+5kNR6PR9XV9V9+mDJlitLT0/1fubm5DX1rCDLCC4BoUeczVH7Q2gZ2rJR0RoMDTFFRkTZu3Kg5c+YEs58GGz9+vKqqqvxfu3fvDndLEOEFQHSZu8baZ0v37FSHOkGDAsyoUaM0f/58vfXWW2rTpo3/8ezsbHm9XlVWVgbUl5eXKzs721/z41VJx/7/VDVpaWlKSUmpt6ekpCSlpaUFfCG87IaX1s0SCC8AIsr4VzdYqn/hln4OdQJLAcYwDI0aNUrz5s3T8uXLlZeXF/B8r1691LhxYy1btsz/2NatW7Vr1y7l5+dLkvLz87Vhwwbt27fPX7N06VKlpaWpa9eu/pofjnGs5tgYiHx2w8tNfduq5N4rg9QNAASH1VUv6amNHekDFjeyKyoq0ksvvaTXXntNzZo1889ZSU9PV0pKitLT0zVixAjdeeedatGihdLS0vTHP/5R+fn5uvDCCyVJ/fv3V9euXTVs2DBNmzZNZWVlmjBhgoqKipSUlCRJuvXWW/XEE0/orrvu0u9+9zstX75cr7zyihYssH85As6q9tapS7G5lWInsm3SACU24i4XANztrTsvDXcLUc1SgJk5c6Yk6dJLLw14/Pnnn9eNN94oSXr00UcVHx+vwYMH6+jRoyosLNRTTz3lr01ISND8+fM1cuRI5efnq0mTJho+fLgmTpzor8nLy9OCBQs0ZswYTZ8+XW3atNE///lPFRYWNvBtIhSGP7dGK7bttzUGl4wARIu8zCbhbiGq2doHJpKxD0xodbpnoWp89v4qEV4ARDJvrU9nTlhkqrZxgvTpQ/xOawizn9/cCwm2sdIIQCyY/d4O07XJXAZ3HEcYthBeAMSKNTu+MV37+0vyTl0EWwgwaDDCC4BYcqDa/AZ2I39ypoOdQCLAoIEILwBizef7D5qqS2kcz0rKEOAIwzK74SUpjvACwH2qa+pM1SXwyRoSHGZYYje8DMtvo61TCC8A3Kdlk6Sg1sEeViHBlDqfoQ73LLQ1BhvUAXCz8/MytLOi/hsK/7gOzuPTBKc0b90e2+Flx9RBhBcArtY43txdpc3WwR7OwOCk+k1dri8rT/0vjpNhvguAaLCt3NwkXrN1sIcAgxPKG7fA8o3LfozwAiBaJJk8i2y2DvYQYFAvlkkDQKDUJHMfmWbrYA8xEcchvADA8c44zdzNGc3WwR4CDAIQXgCgfokmN3gxWwd7OMrwI7wAwInln9EqqHWwhwADSfbDS+tmCYQXAFHtuVWfm6o7P6+Fw51AIsDEvDqfYTu8PHb9OSq598ogdQQAkafaW6dln3xtqvbtT/Y53A0kViHFtHnr9mjMK+ttjfH55IFKiI8LTkMAEKEeWrDZdG3x6xvVv1u2g91AIsDELDaoAwDz1n9Zabr2wJFa5xqBHwEmBp0xboF8NscgvACIKRZ29cxJ52aOocAcmBjTnvACAJbldzQ/MfflP/R1sBMcQ4CJISyTBoCGyWyabKouNTFBLZomOtwNJAJMzCC8AEDDPbz4E1N1f/lZV4c7wTEEmBhAeAGAhtvvOSqvyWvv/1v6pbPNwI8AE+Xshpe0xoQXALHtqsffMV27ese3DnaCHyLARDG74WX4Rbn6+EHCC4DYVn7gaLhbQD1YRh2FvLU+nTlhka0xtk0aoMRG5FsAQGQiwESZ+1/bqNklO22NwSUjAPiOt9baxhNv3NbPoU7wYwSYKNKteIkOeu3tAEl4AYDvzX5vh6X67m3TnWkExyHARAlWGgFA8P1jxWema68+h/sfhRKTHKIA4QUAgq/aW6evD9WYrv9Fr7YOdoMfI8C4HOEFAJxx36sbLNVf1KmVQ52gPgQYFyO8AIBzXv9or6X6hPg4hzpBfQgwLkV4AQBneess3IIaIUeAcSG74aVzZjLhBQBOYvu+Q5bq//eWixzqBCfCKiQXqfMZ6nDPQltjzLjhXF3ds02QOgKA6HTZI29bqu91RnNnGsEJEWBcYt66PRrzynpbY3w+eSDXaAHgFKq9dZbquZQRHgQYF+g3dbm+rKy2NQaXjADAnIlvbLJUP/Ly9s40gpMiwES4vHELZHcaGeEFAMx7a+s+S/V3XN7FoU5wMgSYCMZKIwAIvcpq85vXXd+zDTe+DROOeoQivABAeMQZ5m/g+LcbznWwE5wMASYCEV4AIHxqTc7hzUjmIzScOPoRhvACAOHjrfWpxuTEw5o682dqEHwEmAhiN7y0bpZAeAEAG2at2m661sJ9HuEAJvFGCLvh5bHrz9E1vXOD1A0AxKZXP9wT7hZgEgEmzKq9depSvNjWGGxQBwDBsbnsgOna7KZ8hIYTRz+Mbnxujd7ett/WGFwyAoDgsHr/ozduv9SZRmAKASZMOk9YpCO19iaAEV4AIHiunL7CUv1paUkOdQIzCDBhwEojAIg8R+vM73ue6mAfMIdVSCFGeAGAyJNn8Xfzz84/3aFOYBYBJoQILwAQeSoOei3fc+4vP+vuSC8wjwATIoQXAIhMPScttVTfKF5KSUxwqBuYRYAJAbvhJU6EFwBwQsVBr+XXzPxlTwc6gVUEGIfZDS+Xdmqu7YQXAHDEtU++a/k1l3fPdqATWMUqJId4a306c8IiW2NsmXglpykBwEE7v622VP/oDeexcWiEIMA44P7XNmp2yU5bY3DJCAAiS9OkeF3bk9VHkYIAE2TdipfooLfW1hiEFwCIPOvuKwx3C/gBAkwQsdIIANyjrPKI6drWTRspsRHTRiMJfxpBQngBAHe5cOoy07WTrz3PuUbQIASYICC8AIC79P6Ltd/bnhp7UwMQfAQYmwgvAOAuVYdr9LX5q0eSpMxmyc40gwYjwNhAeAEA97lp1hrLr7kgr4UDncAOAkwD2Q0vnTOTCS8AEAbrdlVaqh/W+3T2folArEJqALvhZcYN5+rqnm2C1A0AwKw9FdY2rpOkv1x3rgOdwC4CjAXV3jp1KV5sa4zPJw8kyQNAmFw5fYWl+vsGduV3doSK6EtITz75pNq3b6/k5GT16dNHa9ZYv24ZLDf/11rb4WXH1EH8IABAGB04WmepfsQleQ51ArsiNsC8/PLLuvPOO3X//fdr3bp1Ovfcc1VYWKh9+/aFvJeb/2utlm62932Z7wIA7hKxH5CQFMF/Po888ohuvvlm3XTTTeratauefvpppaam6rnnngtpH9XeOsILAEQBq/Nf3v7zZQ51gmCIyADj9XpVWlqqgoIC/2Px8fEqKChQSUlJva85evSoPB5PwFcwTF642dbrCS8AEBkGzLA2/6Vtq1SHOkEwRGSA+frrr1VXV6esrKyAx7OyslRWVlbva6ZMmaL09HT/V25ublB62fHN4Ya/lvACABHjkIX5L81TWeMS6SIywDTE+PHjVVVV5f/avXt3UMZt37JhCZzwAgCRpUlSgunavJZNHOwEwRCRAaZVq1ZKSEhQeXl5wOPl5eXKzs6u9zVJSUlKS0sL+AqGewZ2tVSfFEd4AYBItOj2n5iuff6mPg52gmCIyACTmJioXr16admy7+8U6vP5tGzZMuXn54e0l5TEBP20a6ap2mH5bbR1CuEFACLR6S1SlJhw6q0s2rVMUXpq4xB0BDsiMsBI0p133qlnn31Ws2fP1pYtWzRy5EgdOnRIN910U8h7efa3558yxGybNEAP/pzdGgEgkm17aOBJQ0y7lilaMfbyEHaEhorYWUq//OUvtX//fhUXF6usrEznnXeeFi9efNzE3lB59rfnq9pbp9ueW663dnj9jz83pJcuP6/+y1oAgMiz7aGB2lNRrYK/L1f1/83r7Z6Tphd+fyFnXlwkzjAMI9xNOMHj8Sg9PV1VVVVBmw8DAACcZfbzO2IvIQEAAJwIAQYAALgOAQYAALgOAQYAALgOAQYAALgOAQYAALgOAQYAALgOAQYAALgOAQYAALhOxN5KwK5jGwx7PJ4wdwIAAMw69rl9qhsFRG2AOXDggCQpNzc3zJ0AAACrDhw4oPT09BM+H7X3QvL5fNq7d6+aNWumuLhT3z7dLI/Ho9zcXO3evZt7LAUBxzN4OJbBw7EMHo5l8MTKsTQMQwcOHFBOTo7i40880yVqz8DEx8erTZs2jo2flpYW1X+BQo3jGTwcy+DhWAYPxzJ4YuFYnuzMyzFM4gUAAK5DgAEAAK5DgLEoKSlJ999/v5KSksLdSlTgeAYPxzJ4OJbBw7EMHo5loKidxAsAAKIXZ2AAAIDrEGAAAIDrEGAAAIDrEGAAAIDrEGAsevLJJ9W+fXslJyerT58+WrNmTbhbCqspU6bo/PPPV7NmzZSZmalrrrlGW7duDag5cuSIioqK1LJlSzVt2lSDBw9WeXl5QM2uXbs0aNAgpaamKjMzU2PHjlVtbW1Azdtvv62ePXsqKSlJHTt21KxZs5x+e2E1depUxcXFafTo0f7HOJbm7dmzR7/5zW/UsmVLpaSkqHv37vrggw/8zxuGoeLiYrVu3VopKSkqKCjQp59+GjBGRUWFhg4dqrS0NGVkZGjEiBE6ePBgQM3HH3+siy++WMnJycrNzdW0adNC8v5Cpa6uTvfdd5/y8vKUkpKiDh066MEHHwy4Tw3H8sRWrlypn/3sZ8rJyVFcXJxeffXVgOdDeezmzp2rzp07Kzk5Wd27d9fChQuD/n5DyoBpc+bMMRITE43nnnvO2LRpk3HzzTcbGRkZRnl5ebhbC5vCwkLj+eefNzZu3GisX7/eGDhwoNG2bVvj4MGD/ppbb73VyM3NNZYtW2Z88MEHxoUXXmhcdNFF/udra2uNbt26GQUFBcaHH35oLFy40GjVqpUxfvx4f80XX3xhpKamGnfeeaexefNm4/HHHzcSEhKMxYsXh/T9hsqaNWuM9u3bG+ecc45xxx13+B/nWJpTUVFhtGvXzrjxxhuN1atXG1988YWxZMkS47PPPvPXTJ061UhPTzdeffVV46OPPjKuvvpqIy8vz6iurvbXXHnllca5555rvP/++8Y777xjdOzY0fjVr37lf76qqsrIysoyhg4damzcuNH497//baSkpBj/+Mc/Qvp+nfTQQw8ZLVu2NObPn29s377dmDt3rtG0aVNj+vTp/hqO5YktXLjQuPfee43//Oc/hiRj3rx5Ac+H6titWrXKSEhIMKZNm2Zs3rzZmDBhgtG4cWNjw4YNjh8DpxBgLLjggguMoqIi///X1dUZOTk5xpQpU8LYVWTZt2+fIclYsWKFYRiGUVlZaTRu3NiYO3euv2bLli2GJKOkpMQwjO9+wOPj442ysjJ/zcyZM420tDTj6NGjhmEYxl133WWcffbZAd/rl7/8pVFYWOj0Wwq5AwcOGJ06dTKWLl1q/OQnP/EHGI6leXfffbfRr1+/Ez7v8/mM7Oxs4+GHH/Y/VllZaSQlJRn//ve/DcMwjM2bNxuSjLVr1/prFi1aZMTFxRl79uwxDMMwnnrqKaN58+b+Y3vse5911lnBfkthM2jQION3v/tdwGPXXXedMXToUMMwOJZW/DjAhPLY3XDDDcagQYMC+unTp4/xhz/8IajvMZS4hGSS1+tVaWmpCgoK/I/Fx8eroKBAJSUlYewsslRVVUmSWrRoIUkqLS1VTU1NwHHr3Lmz2rZt6z9uJSUl6t69u7Kysvw1hYWF8ng82rRpk7/mh2Mcq4nGY19UVKRBgwYd9345lua9/vrr6t27t37xi18oMzNTPXr00LPPPut/fvv27SorKws4Dunp6erTp0/AsczIyFDv3r39NQUFBYqPj9fq1av9NZdccokSExP9NYWFhdq6dau+/fZbp99mSFx00UVatmyZtm3bJkn66KOP9O6772rAgAGSOJZ2hPLYRePPPQHGpK+//lp1dXUBHwySlJWVpbKysjB1FVl8Pp9Gjx6tvn37qlu3bpKksrIyJSYmKiMjI6D2h8etrKys3uN67LmT1Xg8HlVXVzvxdsJizpw5WrdunaZMmXLccxxL87744gvNnDlTnTp10pIlSzRy5Ejdfvvtmj17tqTvj8XJfp7LysqUmZkZ8HyjRo3UokULS8fb7caNG6chQ4aoc+fOaty4sXr06KHRo0dr6NChkjiWdoTy2J2oxs3HNmrvRo3QKyoq0saNG/Xuu++GuxVX2r17t+644w4tXbpUycnJ4W7H1Xw+n3r37q3JkydLknr06KGNGzfq6aef1vDhw8Pcnbu88sorevHFF/XSSy/p7LPP1vr16zV69Gjl5ORwLBFWnIExqVWrVkpISDhuxUd5ebmys7PD1FXkGDVqlObPn6+33npLbdq08T+enZ0tr9erysrKgPofHrfs7Ox6j+ux505Wk5aWppSUlGC/nbAoLS3Vvn371LNnTzVq1EiNGjXSihUrNGPGDDVq1EhZWVkcS5Nat26trl27BjzWpUsX7dq1S9L3x+JkP8/Z2dnat29fwPO1tbWqqKiwdLzdbuzYsf6zMN27d9ewYcM0ZswY/1lCjmXDhfLYnajGzceWAGNSYmKievXqpWXLlvkf8/l8WrZsmfLz88PYWXgZhqFRo0Zp3rx5Wr58ufLy8gKe79Wrlxo3bhxw3LZu3apdu3b5j1t+fr42bNgQ8EO6dOlSpaWl+T+E8vPzA8Y4VhNNx/6KK67Qhg0btH79ev9X7969NXToUP9/cyzN6du373HL+bdt26Z27dpJkvLy8pSdnR1wHDwej1avXh1wLCsrK1VaWuqvWb58uXw+n/r06eOvWblypWpqavw1S5cu1VlnnaXmzZs79v5C6fDhw4qPD/yoSEhIkM/nk8SxtCOUxy4qf+7DPYvYTebMmWMkJSUZs2bNMjZv3mzccsstRkZGRsCKj1gzcuRIIz093Xj77beNr776yv91+PBhf82tt95qtG3b1li+fLnxwQcfGPn5+UZ+fr7/+WNLf/v372+sX7/eWLx4sXHaaafVu/R37NixxpYtW4wnn3wy6pb+1ueHq5AMg2Np1po1a4xGjRoZDz30kPHpp58aL774opGammq88MIL/pqpU6caGRkZxmuvvWZ8/PHHxs9//vN6l6/26NHDWL16tfHuu+8anTp1Cli+WllZaWRlZRnDhg0zNm7caMyZM8dITU11/dLfHxo+fLhx+umn+5dR/+c//zFatWpl3HXXXf4ajuWJHThwwPjwww+NDz/80JBkPPLII8aHH35o7Ny50zCM0B27VatWGY0aNTL+9re/GVu2bDHuv/9+llHHmscff9xo27atkZiYaFxwwQXG+++/H+6WwkpSvV/PP/+8v6a6utq47bbbjObNmxupqanGtddea3z11VcB4+zYscMYMGCAkZKSYrRq1cr405/+ZNTU1ATUvPXWW8Z5551nJCYmGmeccUbA94hWPw4wHEvz3njjDaNbt25GUlKS0blzZ+OZZ54JeN7n8xn33XefkZWVZSQlJRlXXHGFsXXr1oCab775xvjVr35lNG3a1EhLSzNuuukm48CBAwE1H330kdGvXz8jKSnJOP30042pU6c6/t5CyePxGHfccYfRtm1bIzk52TjjjDOMe++9N2DJLsfyxN566616f0cOHz7cMIzQHrtXXnnFOPPMM43ExETj7LPPNhYsWODY+w6FOMP4wXaKAAAALsAcGAAA4DoEGAAA4DoEGAAA4DoEGAAA4DoEGAAA4DoEGAAA4DoEGAAA4DoEGAAA4DoEGAAA4DoEGAAA4DoEGAAA4DoEGAAA4Dr/H2gAtY1jehhcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([v for v in range(len(parentVTDno))],parentVTDno)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "55d64c64-b473-49d0-b863-d4123cbb9b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for v in range(nVTDs):\n",
    "    if MAP.contains(vtdGeom[v].centroid) and v not in parentVTDno:\n",
    "        print(\"Uhoh,\",v,\"is in the map but not in the parent vtd list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "8bff897e-6fb0-4c18-a201-abc2fce9ba51",
   "metadata": {},
   "outputs": [],
   "source": [
    "outName = \"legisl60_1p5PatchedVTDs\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "ecd50c96-51ce-4d83-b0df-e8c645c35815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "working on full or partial VTD master list for unit 2000\n",
      "working on full or partial VTD master list for unit 4000\n",
      "working on full or partial VTD master list for unit 6000\n",
      "working on full or partial VTD master list for unit 8000\n",
      "working on full or partial VTD master list for unit 10000\n",
      "Now converting unit lists to vtd lists for all HDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+outName+\".csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "5075814f-4a75-4807-ad8d-b3cc969cb470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8941 8941 8941 8941 8941 8941\n"
     ]
    }
   ],
   "source": [
    "\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDvPop = HDvPop[0:nHDs]\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDfullVTDlist = HDfullVTDlist[0:nHDs]\n",
    "HDpartialVTDlist = HDpartialVTDlist[0:nHDs]\n",
    "HDvtdFrac = HDvtdFrac[0:nHDs]\n",
    "hdCPx, hdCPy = hdCPx[0:nHDs], hdCPy[0:nHDs]\n",
    "print(nHDs,len(tList), len(HDweight), len(HDvPop), len(HDvtdFrac), len(hdCPx) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "e385f357-5b7e-46f5-b054-c5c38d799395",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999998 8941\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight), nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append the cut districts and adjust the HDweights\n",
      "99 0 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append the cut districts and adjust the HDweights\")\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"legislPatchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "b0b611f7-6c35-495d-b38c-c848317d63ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999998\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "\n",
    "plt.scatter([v for v in range(nVTDs)], vtdUSE)\n",
    "plt.show()\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "3d2a393c-5ab8-4140-80eb-1153c2ef41f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u]  < 0.9:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "    if  unitUse[u]  > 1.1:\n",
    "        plotPoly(unitGeom[u],1.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops\n",
      "x = n surrounded, y= unit - vtd-based\n"
     ]
    },
    {
     "data": {
      "image/png": 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/F0xk6fW65/7tAtYxpPvmFwpYetKpXvM///NSlp50Gz1KMH2h1108uZB1DOlKi0az9KSbVcDbBEk3aUwqS0+6L8/mhWD1ul99hXcfSDd1LO+Lm3T/Nof3WSXdfQsns/Ski4+14/Ipxu/r5VOyXWW1P7p+Buv8et2ry+axjiEdlUabQbrcNJ6hQ7o7Lik09WrZ+nUAMDad593Q637+zz2sY0h32ZQxLIPwsilOYydzRDzr/FydJ/peLp7Lon/re7kMF4Iemrn//vuxatUq/PGPf0RtbS3uvvtunD59Gl//+teD/dJu3DyX90TE1XlSWMT7MiVddyrPYtbrMnJ5HiLSTSucwNKT7vQZ3i8P6fqY7dRJ938fD84L8gbp/vgxb5KuXvfIBl6cm3Svb+OtiXRv7eZVkJCuMHsES0+6TzlNUDx0R9qMs/49daOY7mjSZTG/VElHT3VG6J/qYuw2PHPrTEP9M7fOdPvyjY+1m4aAEmLtro38osIMpCUbX3d6cpxrTXnMTZB02w7wqnhI1+fQUG3SU6f6SIerOiIpPgaLio0Nl0XF2UjShSkyRsZj9Ejjezd6ZDwy+jWTmHOaSJeTyjNESBcfa8ddC4yN5rsWFA60nlefeYn6ZmZ1VL8uxm7D/3zN+LP3P18b+Oyd6OQlenN13igrycVvb52JHI/foZzUxIgfnGiVoBsiN998M37605/ihz/8IWbMmIEdO3Zg/fr1gxJYg00TswkXV+fJ5j28DYp0bcx4rF6nmry14VNmcli/7iAzcYt0f97Ky+EhXfMpXuky6U4xG7LpdU0nea9BupZTvKQv0tU3894j0n3A7G5Lur9u55VQ63X7jvOSBkn3P+9+ztKT7pMjvE2WdPRUZ+Re9nyqKyvJxTO3zkRGsrvbLzM5zmsb7y31raaTlrt7HUrVBfp9T9WYUu2FYqU3y6rbZvs0Rnw16vroB4t8GiOeTdBUw0Wq7xHgnFP1jQWFg17HbsOghmbNp5nfFzpdUhwvr0Svo89e9kh3QzV7ZPygz14G0yjn6nxRVpKLzQ8uxEvlc/GLr8zAS+VzsfnBhcPSCAFC1Fn13nvvxb333huKl/LJd16vZuu+5GXAmRmHOng176RjhmPddIeZT76kO3KCOXelX6faWXU78ymQdKqGlJUkRgczR4x0zB5uLl0i88aRTsWwmD9pNE53Mxus6XQ2G++dIt1Rkw2QIN0bO3hG9hs7GnDPF5weRXqqU+0OGR8bA2Dg2uJ8eD1Uqwu21LeyGtFRF1AyprzNUQG8GVNqn1ar1RGrbpuNMz19+PG6Guxv6URBZjK+t7jYzRPiyePXl+CHb3zqZqRnj0rAY9ed76aLj7WjfH6hYa+P8vkDHgv198jJisXFeODKyUEZwnfl+TnYUGv+AHbl+e75KmUluVhUnGM63FDVC+QPMXZbWAYkhoOoGXrHrK5j6/ylo4u3A+p1J7t5x5COm7dNurPMXZl0HV089yPpVO9BD/MCuDpvpCXFoLnT3EBKS3J+0RdkMRMA+3UnmYYF6Sx4ozEtLxXVR8177kzLc+ZkpCTGDpqT442UROfXw1kTzwPhqeN+uQO+h6cd6+j2OjxNdZOysvGrGFOlEzPx63f3mp6fNhZ/qiPiY+1YPHWs6z313MD1+Hpfm056f1/JI+HNGPH0WADWDU4OVobwqYbU9HA2fqut/AVjosYQiTRUjYRQEGfjvRrprBhTwUb1fVWdKdTcwexu26+zabwXIF12Ci93SK+7qjgHf9piHia7qr8a4d/nFZl2hyQdAEwcPQL1jLDdxNGD82E4X+5WJg6rbghWN36uMTW3KBNpyXGGXpf05DjMLcq0tH6CO/UZMH5fAd8zggDne+45a8YXZSW5KC3Kwr//YQuOtndhbGoi/veOi5DqIyeHO2tG73HxhafHJdiGglUvEOC8HxyjPBqJqD4iQng5wew+z9UNBRy9vHAR6RxMw4J0vcxYEekmZvHKRvW6NmYuDenyM3kJtKS7ivlUy9V5YqWbZIzdhiXTjV9vyfRcN8NFJVlVDxlT180Y5wrbeNPcfGGe4flvujDPdazq+gG1qc+AtTwUMhJ8zZrxnH8DAEt+XYHpj72NrQfb0NDeha0H2zD9sbex5NcVg7Qqs2aAgSF83nJKPIfwAdbyk1QpK8nFFT5yda4ozvbqBVpf3YB5T23ELauqcN/LO3DLqirMe2rjsJwbYwUxRISo5hQvcuLStTAT6EhX28hLJCXd0tICmKV82HQJgwCQNYI5QbhfF+wKElWshE36HBrW7DT+El+zs0FpJoc/Udk+h4bnPzBuef78Bwdc6+lzaHjlY+P8oVc+Puym50x91l/vUWZOGelUZ8EATiPkk8Pew4KfHO5wM0asnH99dQOe8zIYUNOA5zyG8BEULvJMpM0NUNXJynU1hkP1PI2paBxip4oYIkLEEokdN5PjedFM0vUyPS6ki7HbEGcyZTk+xu72RKfqpeGgV87KT2dVU8zKH9yDp8+hobKuBW/sOILKuhavhoGVsInq075KsqoVNu857raBeqOn14HNe44DUJ/irKoHgI8P8NqMk+75fxkbCQTpTnX1+jRCiE8Od+BU/+TqVRW8mUik82cAXLCqTlSHFUbrEDtVosYQyWXG3rm6oUAG81K4Ok/GjOT1oyDdSGbLZtKlJ/M2fb2uJJcX2iDdiATea5DuokJeFjvpVHt2VO1rMd3QunsdbkO1qup5Gw7pVDflrQdOsAa0bfWoouK6o610k1T1ogR7hsdzm3kD3UinWu6rqgeAz5jeONJtqOH11CHdt1/ZztKT7qUtvL5ApPN3ABwnpKaK6rDCaB1ip0rUGCJ/WXaJkm75Il6rYNIx90yXTnU2BQAkMg8iXXYaLxeAdKprKsjkbfqk+9JM3tMI6a6ZzmsJrdddxWwjTbrbZ/OavpHu9osLWN0hb7+4AACw0KQJFUG6f+3lbTh63SHmQDTSNbbzXPaks7KJq7ijrXSTVPWiBHuGRwczT2dAp+rvU/cPMqu62TpPDp7gfY5IZ9b3hSBdJA6AUx1WGInXEIlEjSGyrvqoku7NT3l60nUycw1IZ6Vqhlt8Qjrlvh2KX1ydzLAD6Y628fIrSHfyDLNcWafb1XCSdQzpDnTwvgBIF2O3GfZsAJxdMGnTzE3h5U2QTjWuDwDNzCZupFNtLKe6iVtxR6t2k1T1ogR7hsf0vDQlHbc/BOlU9YCV2S5q+gnpvFJ20k0fx2v9T7pIHACnOqwwEq8hEokaQ+TDfTzXF+lUnzIjEdWeFPFMQ4R0xTmjWHrSqT6J56Twfjn1OtXJr51MY410W+pbTY/p7OlzuVqzRjETSft145jzO/Q61Smlrcz206SbMT6NpSedVXe0Slxf70Xxhd6LEuwZHt+/xngtnjoq9zVCX+6rqgfUZ7uo6n9+M2+uE+n+v68Yt1InSBeJA+Csdp+NpGuIRKLGEGlgboKk62ZuUFxdOGB6Ql06pgPCpetjmjqkaz7N7MHRrzvFbAam153p4R1Dugsm8J7SSKdqTGWPZPYF6dddfA5v8Jhel8002EjX0MbzApHuTx8aV4MQpPPHHa0S11ct7QzmDA/VWTAxdhuevMF46vDKG6a6GVIqekB9touqfmRiLKblGc+nmZaXgpH9jfFU9ZE4AI66zxrhrfssEDnXEIlEjSGSEMt7aiSdaqOrSKSP2cOCq/Ok4jNePgPp4m28N8uls1A2c4ZpfZEuxsb7FSBd8ymeN8GlU7wGK0++aUm8uRakG8d0qZNONS7ujzuaU2VDWC3t3PDtS7FoSjYm5YzCoinZ2PDtSw2NkJ5eB35fsQ8/fKMav6/Y5zOZeNVts31utNPyUgbNgqEZJ56ev9zURK/zdVT1gNpsFyv6NffON7zmNffO90sfiQPgVN+jSLyGSCNqOqvmZSRj26E2lg6IvJbwVojReN4ars4TVRd/KzORhnRjTCaHEnpdXgpvkyWdcvJZB88jQjrVfAx68l1m0E3S88nXznycIN3cwkz85l3zUsq5/ZU/45n9QUjnT9dQbqtwK91YAaD8hY/cekDsaTyJkkf+4XNo3Mp1NVhV4W7sPLGuFuXzB28466sbsMtHOeuuwx1YX93g1bjgtsG3ogf4s130+v+8YhJ7ns2ae+fjVFcvvv3Kdhw8cQYT0pPw85svcHk2/NVbueZgo/qeRuI1RBJRY4iUjE3Bmp3mCaglY53WeqyN5+2IjeDP0bHTPAODq/PkLPMw0p0+y/NWkG7NDl7C8JodR/H/rnBWL33awMsFIl0TM1mVdG/XHGPp3645hu9/8fyQJKuVFmXh1wzDorTIGc6xM7OSSTd5DC8XiHTUNdSoeZW3rqHe2mZTlY3nk6NKHgolcHoaIXo21DSh/IWP3IwRX11AHdrALBYyRvxppx6K4WbxsXbcOb+IpfU0CCs+B/5Z22Q4O2ZkYixW3T7YkPOFqj4SB8CpvKdAZF5DpBA1oRmjwVDedKr6SGSoe3VUpw0DQHUDz8NBuowRvD4fpFOdjjuJuYmTjjY0IzwrTqYzk0lJp5rn0sz0fJGO0zX0zx5dQ1WrbFTzUM709Pk0QogNNU2uCjLVxlVW2qlbIditwqULqBAOIncXDTA/27BHSZcQx8wp6ddZ6QsiGNPNTMDh6rxRc7RNScf1pJKu/IWPWHrSWdnQnlhrbLh46j5mboakUy0Prtpn3gX0ROdZV1M2K1U2qp6mJ9Z+ytKTTrVxlapxZ4VgGwnSBVQIF1GzL55hzoon3TVTx7D0pBvN7DLK1Qnq5cdW2N14WknXyfSIkO7wCZ6HhnSHW3nr0es27uaFi0hXUcfrxEq6ltM87wPpKpnnJ52VKhvKQzFCn4ey41A76zVIp5o71HqamS/F1HkSCiNBuoAK4SJqDJGkeN6lku4DZodL0vUwm3uRLon5ZM3VeSNvFM/oIV0C87W4On/hRr30OtVCG9US5y6m94V0McxKIdKt/5T3VKvXmbWE99Sd7OQ9lZPu6AmeoUA6h8ZbD+ms5NFYmV6rgmrjqgxmmbY3HadSKBRGgnQBFcJF1Bgii2fw+jOQbl8L75eNdCeYbU9Jd4b54KLXqW6yx07y+naQLi+TOWWVqfNENXw1ZhSzakanK8zgHUO6OOaiSJecwGwe1q9LjOeth3R7j/M8InpdTiqvUoh0ZzXeJ4l0TaeYG1S/Li2RWU7cr/M2LM8bel2fQ8MfTKbd/kE37fbiibyGUaRTbVxlpfkewM/58NdICNbwQUEIBFFjiLxcxXNfc3XhQDVUwTNDBnR1zTx3NFfniWpb+0TmpFu9rrWT55kiXXoyz7Ag3ZQc44ZMBOlUG+md7uatX69L9VH26AnpuN570h1qYXYZ7te1neF98khX+TlzoJtOt/mz46azS7p7Hdj8mXPabeZI3uZJOtXGVaqhIkAt58MfIyGYwwcFIRBEjSEihB9V74ONWWaq1zErhF06m523iZNO9cnXpvF2fdKlJ/HCaXrdIWYeCumYS3LpVA2Lo0zji3Sqk2sBYFUF7xjSdTCvQa9TaVxFHTRt8N5B0wb3DpqqOR9WjYRgDx8UhEAghogQMs7N4oUQSJfCTEbR6+xMvxHpCrN4E4pJ59kd0Rekm5DJHAzWr5uSyyv31evimMk0pMtN4RlfpBuZwNOTbmwa7z0infrkWnXjyOok2hWLi7H78avx0DVTcFtpPh66Zgp2P371oGZmgFoHTdWcDytGQiiGDwpCIIiahmbLLsvHM++Zz8xYdll+CFZjDRt44ZlIfV5RLXGsOXyKpdfrHMzETdJlJPFCM6RrPsW7BtIVjU7G7mPmHoui0c68my9Nz8OaTxpN9V+anuf6++wJGdjfcsT0mNkTnE/LJXkZ2H/CPARZkufUX3beaLyy1fz8l5032vk6+RkAzD0WTh0wbVwqdh3x3pFUzzTd9Nac1ARwBmrnpDqTQ1WbvulRaVzF7aBpJeeDjATP7rM5PrrPWmn6pnINghAoosYQufTcXJYhcum5zl/mxSVjsK7a/Mt6cYmzfHdBURo27Wsz1S8oSjPV+CLODnCqkLkhkFDTymtH4dJZSeg9zQzNkG7tp8dZ+rWfHsdvALz1qXFTLOKtT5vw3wDWV/PyH0i3p+kkS7+n6SS+cL7zs6daXjuCmVNCuvxMnteIdBtqzQ0p0n1hyhhcMWkM/m/LIVP9FZMGSuoXFefgn7Xm925R/8j6uROdM3yM+pukJcdhbgA6X3I6aGYxq2w8dSpGQiCGDwpCKIjQLSvwXFSYwcqCpxjr1Lw01nlJlzqC547m6ryRyGyyxtUJ6nQzk1BIp5qg+09mTxC97rMmXqUN6VpPMWf+9Ot2HG5j6UnH8W7odVsPn2Dp9bpTXczJzP06zvTaJz1m+AQVP5rkcCcUh2r4oCD4S9R4RNo7z7I6JbZ3nkXGyHg0nWQON+vXHWG2Iyfd6BFxOH7aPM49WteC3M78kuTqBHVGJtjRzijVHpngtPFVw2lW8iViY3jPE6QbySxBJl0ys3qJdClMj8uATn3McsYIXokwV2dEn0MLeJiiiTkM0ZuOux5KcG1s7/L6GbTBGdbxZ/ig6poEwRtR4xG5+r83KOlerjzI0pPuONMNSrqEGN4vqV53tpf3FEg6rl+EdKp9PnjOZb7Ok/MyeRUket3oEbyrJt2kLN5GRboZ43jlu6TLG8VbD+lUB8wBwFXFvC7ApLthZp6JEm66G5l60s0/dzRLTzpuCECvU+2dYjbDh6b1ej75B2u2SyvTEPHUqazHSoKrlTbywZ5/Iwx/osYQOcYc8UC6M0yfOukOtfG+WEh3uIPX6lmvYzhQ3HTcmbqkY9pGLl0fU8/VeeKw8wwRvU419p6UyHNfk67PxjMsSDd+NM9wId2XZ01g6fW6BZOyWceQ7uJzsmDmRImxO3UAMKeIZyiQbgzTSCDd3CJn/oYR6clxmKtbx0WFGabHpCXHuZ72rXQmDeZsFyseHSvrUamCsVJlI0PyhEAQNYaIYI5qPkMs03Lh6jzpYy7IXafm5j/VzbPuSFfATNwk3QSmnnQXn5uF5HhjYyc5PgYXnztQ3TG3KBMjTI4ZkRDj2sh7eh2m722fY6Al/Ef7eW3DSZfNNAZJx8nfWGkhf0OvVk3cDPZsF6seHSvrKSvJxeYHF+Kl8rn4xVdm4KXyudj84EK/qmw4a9IM1iQIesQQEVz0Mb8vSDdxNG+TJZ1qQ7MUZv8Kva6VWV5LOrMpsQTpZutKSI0gnY1pTJEuxm7Dz26abqj92U3TB23KZr1E4nQukB+v403rJZ3qEDsLKR/KbKlvZU34pU1TNXEz2LNdVDux+rseToKrqrFmZVK0IHgjagyRknTetx5XJwCHWnjVGqRT7Xp6oJXXR0SvO8as3yVd82le3g3pvr+Wt4mTblcDs4KEqfMGZ1Nu023K+5kt2wd0aiUezcz8B9L1OTQsf22XoXb5a7vcnqxVN03VjT/YA+D0nVi94dmJNRQD6VSNtcYO3mtxdUL0EjWGyIzzxivpmMN62brhyOke3gbF1XlyqptnVHB1gaCrh5d5M6BT28StJFWqblIFzKGFpPPW5MsbpFPd0KrqWliGVJXOM6P6GqrTekMxAI7yNzwNpFwv+Rv+rodTjqvaRt5qwq0geBI15bszJ6Rj9YfmlTAzJzgnfJ6blYRPm8zd/NSOPBYA59l6OL3hdjsvj8NOs2OgVspqYx6gb8sdZ+d5Xqw2fYuPtaOX8QI0DK0gMxnVR82blNGmb6Ubpuom9WDZFLxYZf678GDZFADOZmDJ8THoNDDCkuNjXM3AZuWnw2Yznmljsw1M063cxxx6t68Zl/TnxlCyqlmDMto0+xwaXvn4sOH5//zxYfxX2RTE2G2WS19V4TYo82c93HJc8tLcvXrboN9Vb1U2oSyhFoY3UfM8n5vGSw4jXVs370mWdBOyeE+ZpEtJ5FVf6HWqoXfmYFmXTrXcN43ZL4J0Y1N4X0ikS2Qmuep1f793AesY0mUwL5p0V53PK00lXWwM7wVIZ8UFr/oku+tIO+s19Lp4kxyUBN3/f1TfajpYT9OcuoEVcrCerFq1z9zrcqLzLKr2Ob0uoRwAx8nfoPX4els1H+tRrWpRqbJRTbgVBF9EjSGiGiPucfDc/aQ73c3LNSDdOVm8p1i9TrUZYzyzwyrpVMt9Wzp510y6JJPKDoJ0McxGXXrdxs94nUlJ18t8j0g3IoFnTJFuLPNLmHRWXPD6TdMX/uQbqCaGqng4AGt9RFTX9K+9vDXpdUN9AJzVShtulY3qd6og+CJqDBHVL+vpzOoI0o1iVniQrqGDt4lzdd7oOsvMZ2DqPFEt91X1AmnMJ2C97m/bjd3vnjo7cywr6VISeYYI6dJH8HqhkM7quPeyklxcUey9n8gVxdl+5Ruoe2nUPBxW+oioDlA8yux87KnjbsrBxkrukD+VNipeGm7CrSD4ImoMEVV+8m8zlHRpI5hhin5dYwcvgYur80YX077g6vzlkyO8KhjS9Zn59/vR684ya5BJl8D0iLh0NqZfql+n2mDNqgt+5boabKjxPpBvQ00TVupKdlWNHVXDRdXDEWO3YXZBuqH2woJ0t2tuPc1rCEi6cczQrDddn0NDzdF2bD1wAjVH2037YvT0OvD7in344RvV+H3FPlc/Fl9wEkmtGBWhqLRRSbgVBF8Mp9xJQ7hPFIuKcxBjtyk9Wd85vwi1DbxNlnR+zLwKGgkxQDfDKGGOKhlEL/NiSDdmRBxOMRY0Rud1uKggA/XN5uWpFxU4N9nFU3Pw/L/MpzIvnuqc4pqezDMsSBeK6oueXgdWVdQbalZV1OOBKycjPtbuMnaWrd7mVetp7JDhYrQR6g0XSvg2g3Q9vQ68U2s81fid2ib09DpcuSoZTAOPdBefk4XfvFdnqqdussTKdTV4blO92+/hj9bW4q4FhVixeLCHdeW6Gjy7yf1ePL62Ft/woV9f3YAVr27DCd3zRnoCsPLL7pu4FaPCn89eT68DL1bux4HWTuRnJGNpaYHPPKGyklwsnDyGrbeCzLIZ3kSNIaJajXCglddrgXSdPbxABVcXDhJj7ehmlMEk0maQHItWRp5IRrK1j9n4rBGoazX/Ah6fNdBYTbU0tbOHF/oiXdYopoeDdIp5mH0ODd96eYeh9Fsv70DtYzmuL+IXK/ezBjq+WLkfd84vAgD8dZuxof3XbYddG2GM3YaScSmGvz8l41Jc6/nTh+aGHenunF9kaf05KbxNlnRzizJNq7Zs/TrCm1GB/nPQz/XGhS89fOjXVzd4NQZPdAPLVm/DMzqPghWjQrWySH8dqyrq3e7JE+tqUT7ftzHlWZXzu831hkPyVLAyhE8YWkRNaEb1iWJ8Os+VSzqu8U865mw2N51qVcukLN6mSTqz1uIE6WYVpLH0pItjbsqka+/kud/1uo/qeV1ASfdZI8+TRTrV9uVNJ5lTVvt1m/ccN3Xl9/Q6sHnPcde/65p510C6Mz19PsM4xIaaJpzpL9ft6XWw9LTuz4+blyvrdap6ACjO5c3wIV1751lT76LWrwOc1+zLqCCe3VTvumZVfZ9D8+mRIpat3uYK01CpsxlcHeH5K0nGlKdh6NCc61/p0ZU32LNmZJZNdBA1hkjWCOaTbL9ucg7vi450E5iGC+n6mG5FvU61qiV1JO8pinQJcTzPBel2HWpj6Ul3lhmaIV0jczCgXrftIG9NpGs/zTMUSFd7mNcBlXR7j/NKZUn3XIV5+MBTd4yZuEm6x9ZUs/Sk+/3mfSw96WoOn2DpSbf7KO890uv+8+WtrGNI9+VnNrP0pPvdJt59IN2v3/2MpSfd29uPsvSk+8f2Iyy9XqdaWcQN8emNqWDO4wn2+YXIIWoMEVUXeSvzaZx0d86fyNK7dNxfHj9+yQ608DYo0qmGNVTbo6vOmjnNHEin17UzM29Jd9hkVgZBuh/9o5alJ93vN+1n6UlX18xrm6/XtTHLqEn3jxpeiTPp/sbcBEn32XHe5450BxjhN0/dlv08Y4d0+1t5ayLdn7aYN3zT6557j2eske67r3/C0pPu/r8Zt8An9LpDzPAy6VRCZEDw5/EE+/xC5BA1hojq/AvVmOxpZutv0lmpaIllGlOkO8MsyyVdt0lIgCBdHLPPB+lSk3ilrC4dNxlNp2NW47p0vcz7wNV50s3M0CVdLPOaY/24Zu4TJOnOMscgk071/NZscrUnC9UybU6ulF53lnkRpDvNHLxEOtXzA8ArH/GMKdKp5sUFuyonFFU/QmQQNYaIqmHhzMo21sbY4Ur0OtHJM3S4Om/kjOLlcJCuj/llSroGZqkw6SbnjGLpSTcxeyRLT7rx6TwPjV6Xl8oLwZFONbdHlQSmG4h0MyaksfR63aLiHNYxpJuVz+uRQ7qLC3nluKSbkM77XSPdzAm89eh1F5mU+3rqLi7iNdUi3TRmHyHSpTGNbNKpGo/MJsNuupNdPI8i6fIzeL9vpAt2RVgoKs6EyCBqDBHKIDciXZdBfqqr13SOSp/DqQOAk0z3OOkSme4Nva6ti9l2voueNHl60vX18R77Sffc0tksPemmj09j6Un3/y47l6XX637wxRLWMaS7+wtFLD3pvjTde9MwT0j339dPZelJd/OFE1h6ve7rlxSyjiHdL2+5kKUn3Q+uPZ+lJ92DZcaNAwnS/eqWWSy9XverrzKP6df95mu8aybdL74yk6Un3dpv8UYLkG7meJ4RT7q7Litg6fW6UYk844h0S0sLTJ2QdptTB1hvvscl2OcXIoeoMUQAGA7tAtzDK19//kPWOUn3f8yYMum4Fa16nbqLnPcapDvFbFNPur8wXb+ky0hmDsnq11XUHTdRYpBuBLPDLenK5/OMHdItnDKOpSfdvCljWHrSTctLY+m5Om+MTIxFvMkjdnyMDSP7ZwQlxcdgkY+urcSi4mxXa/7TzJAg6UYmxmJannFy+LS8FNd6rBwzMjEW+ZnGCeX5mUluepXz56QlIsnE+5UUZ0dOmvPp/f8tnGyoJUj37cunsPR63U2zeRPHSRcfa0f5fGOjtnx+oas/SLDn8YRy3o8QXqLGEPng82ZWWeQHnztnTdQ28DL5Safa7ryzj/fLo9fZmHFx0ql2De04w9tASPd/Hx1i6Un3KbM6gnT7jvPKUvW6ZmYVDOleYhqQpHtkDS9pkHQPvLqDpSfd/X/eztLrdX/4l3Glg6euvfMsekw60Pb0aa5SVgBYddtsn8bIouJsrLptwDtmZTz8mnvn+zQU8jOTsObe+YN+vube+T6NhWl5KW7H9Dk09Jjk6/T0am75Ld+87BxDvef/1z5+tU9jJCnOjtrHr3b9e955o1mDBOed5xyeGB9rxzcWGBsJ31hQ6HbOCRkjDNQD6HUrFhfjGwsKB3lG7DZ4bcoW7Hk8Q33ej8Ajahqa/ZXZKfWv2w9j/qTRYDoH2DpPUpNi0XXWvDInNWngFo2IB7oYyf80dXtMSiJOHjevwhjT3/TJZgfLorL1f9edZFa1kG5PI69fBOmaT/Eql/Q61ZbqW/bz+o5s2d+C8gVF6Oji3XDS7W7gXTPpPjnCKw/W696uaWQd83ZNI+66dCL+/Q9bWPp//8MW/PWbl7j+veq22TjT04cfr6vB/pZOFGQm43uLiwcNM7QyHn59dQMO+qjyOthyBuurG7xuOt+87Bw89LdPcFxXwTV6ROwgI8GsAgNwb2io2omZqH38ajS2deGLv9qEjq5epCTG4u//b4HLE0LE2G345VdmGPYS+cVXZridm4wAb/1KvBkJqh1x9a/zwJWTlTqrLirOCVrn02CfXwg/UWOIHD7BywgnXYwd4AzgpYTWGBvAGXNCHvH7L5uIB9eYl4Lef9lAWbDqHJXLJo3GXoYhctkk51NXzqg4HG43Ny5yRjljynlpSWg/Y77R5vXP79CYDetJp1qV038wj35dJ6envU6XFB+Ds4ySJ9qczzKnOJMugZk75K5TqyA5yixZ9qZLio/B4yZ5L6rj4Y36RRDeNn1qduV5XPPpXty9epvbE7OVicMqnZjdristER8/dKXpa5WV5OKZW2fih69Xo0lnTI8ZFY9HryvxanipGAkU2vD2HgHGQ+niY+2uLrYcaEhesAj2+YXwEjWhmbw0XkY46VRnWRRk8L58SffE27wGSHods7+XS6camrn4XF4+A+kWT+NVa5Aui9lgjXSq7dcB9dDMVJM8AIJ0N07j5YiQ7vwcXqUQ6b46m5esqteZ5W946nJTeB4Lrs4T1fHwVvpFqDa7Cv7EYWuUleRi8/LL8dA1U3BbaT4eumYKKh683DDkEGO3oXhsKmblp6N4bKqhZ8AV2khx/z7LSUkIaGjjTE8fHnp9F5b+/kM89PouV1fecMIZJhjJhGL9kfIeRY1H5MaZeXh9p3k3wxtn5gEAHltcjG+8bB6vf6zfHfrq3fMw80cbTPWv3j0PAHCK2UiEq/NGErPmlHTcIVWkG5vKM+5IpzqnxVnqaB6L0pdOqm4488/JxjPvm+dYzD/HuYmfYMbiSFd6TjY2fmYe/intP/9/LJiInzCM1P9YMOAp+/olRVj51h7TY75+ifMJd+GUHGw7ZB4CWjiFZ2h6ovokbmXTV/VYkHHU2N7lc005fkwctsr66gY8sqYGjR0D17Kqoh6PLPE+R8X63BVf6Z7+U/7CR24jACo+B16sOjgodyiUDPX5NKFYfyS9R1HjEbn43CzTWSrJ8TG4+Fzn9M3ERJ6NRrrU5DjTX21bvw4AYpiNAfQ65igYl+4t5hwG0hVm8pLbSKfqgh+XxvvSJl0Bcz16nerT+OzCDNOeDjabUweoh5duv7iAdf7bLy4A4DTyOBUqeqNRNZFRdf6NFVTGw1vZ9FWNF9UKjFCUjtLQO70RAgCNHV1Y5mWOipW5K3SM52sc6wjMrBZPI0TPhpomlL/wkV/nt8JQn08TivVH2nsUNYZIjN2Gn9003VDzs5umu76I/raD2da6X7elvpU1VIvcyzdeyLM49brpY3lhBNIdbeO1tSbdzcywAOlUB3GlJ/M8IqRbMiOPpdfrYuw2LJlu/N4umZ7rus9bD5yAWbsVTXPqAPXqqPhYO+4yKYm8S1cS2efQUG2SsFp9pGOQC5WqHbzhmcio2rjKKmUludj84EK8VD4Xv/jKDLxUPhebH1w46GnLyqZvxXhRqcAIdulon0PD8teMK7BWvLbLdZ+tzF0J9qwW1eGJoWCoz6cJxfoj8T2KGkMEoOxr3yWI+i+i00wXPOkamYPHSDchjZc7oNeNYea5kK6NWY5LOpXx7QDwYR2v4oR0HcxOj6SrY05l1ev6HBrW7DS25tfsbHD9kqk+WbcyvQR63YrFxYZlpnojQaW6w5MVi4tR/chVWDQlG5NyRmHRlGxUP3LVoGoK1cZVenp6Hfh9xT788I1q/L5in2lJPCUZXjdjHEonZnrduK1s+lY9FlzjiLTBKh2t2tfCGkhXtc/5u2MljybYs1p+vM53VZEVXSAY6vNpQrH+SHyPosoQWbmuxtCNqB9xTSWtZpDuaDuvKod0/9rL28T1uuPMTZN0igUk+MenvDJQ0v1lK6+PCOlU+6Dsb+ENgNPrVDdy1XLfZGbDNL1u5boafOJjau8nhzvcPnf+JEmuXFeDaY/+Axtqm7Cn8SQ21DZh2qP/GDS6XbVxlf78kx96C4+vrcULlQfw+NpaTH7orUHnt4Lqpk/Gi6/PuAbfHguOcaRfF9dwUaGSacSTzsrnItgJt/tbeN95XF0gGOrzaUKx/kh8j6LGEFEdcX3BBF7YgXRv7eJNNCVd+xmed0Cv+/wYr8cEV+eJZxzZTFfLfB3SzWHG00nXxJx9o9c1MMNRLp2itaY6g0T1c6dqGBEr19Xg2U31gwbIOTRn3wlPY0G1cZXq+a0QrE3fX1QMFz5qHzwroahgJ9yqTusOBUN9Pk0o1h+J71HUGCKqI67HpvESMUl3jGk9ks7zyc8Xet3pHt6XF+nSEnhfmKRTnX+TwpxlQTrVCajZTK+UXvfxAd6TJulUja93anlJXKRT/dwpu7GgbuwQKxYX49NHy7B07gTMPzcLS+dOwKePlg0yQqye3wrcTZ/bcMxbnDvYJYuc85cWZbHORToroahgJ9x+z+Nz4q8uEAz1+TShWH8kvkdRY4iojrielZ/OiqNTImYK02VPuuJcXuKpXqcxh9iRbuxI3pq4Ok++cB6vfwXpGph5NKSzUjWzp5HXFp50Ow6dYOlJV+ej+6cnpFP93Kn2QQEsGDv9rK9uwML/fg8vVh1ExefNeLHqIBb+93uDMuatnj+YWI1zr69uwLynNuKWVVW47+UduGVVFeY9tTFgVQLc88+dmGk6hDMtOQ5z+5t4WQlF+RO+4qA6gygUDPX5NKFYfyS+R1FjiKhWCmw9cIL15UvVFHXNvA2HdL/cuJel1+t6mA+cpKtp4YV/SFfXzNxk+3UH23g5HKRTzUGZnMObUOquU3MpcB+GSZcQy2wS168LxWj1emYujV7nq3yvwUv5nqoxFQqsxLmDXbKocv4Yuw1P3mDcofbJG6a6bQYPvV5tqPf2//f+yXcLec7/m6EygyhUDPX5NKFYf6S9R1HT0GxpaQGeWFdruPHoKwUiMaEn0vj0CK+qhXSnmZ1SSdfayWslq9elMsNFAzo1w+W2ufn42T8/N1XfNjcfgPrnzsp8ELUG7+Yt1TW4t1Qfn84LU3J1gUA1l8asZNHX7BguVs6//aCxN277wROuDaH1VA+Om8xeOn6qB62nepAx0tkR92BzJ8yiZb0Op25ClvU8Du4MolAy1OfThGL9kfQeRY1HRLVSQPXJlPtGDqc3vE/juWhIl8hs4kY6K96BtjM844V0KQnMPJd+3fTxaSw96ayMVlfpgwIAF4xnJlb361Qriybn8MKIXF1AUMylCXbJour5VfNubn7uA9Y69LqyX7zHOoarM4JmEL145xw8fv3UsBohRHCSjENHKNYfKe/RcNoXTaFKAW9xMc9KAdWEnpf/o5S1BtItmMjbPPS6uRN4vUdIVzKaNyuEdEum8lp6k445z82lO8NMZiTdRYUZrDi63jtwijnEjnR25i8e6bgblV5nVoGl//8+h4bfbTbeoH63ud4tATKXmVhNuqPMAZCks+KZCjaquTTB9nCqnl8178bKoMLOszxrjasThGARVYYI4PzS9+wRMiYlcdBmoZro9UwFL+eDdC2neF+ket2BNt4XPemmFvCG2Ll0XGu4X7evmZcYSrpGZjkuVwcMDktkjOB5OFw6xSSRvU28cBTpVKs73q1tQp+JvdbncOqISWN4uTSkq6w/ztKTLiOZZ9B6053q6kX5Hz/CVf/fJpT/8SOc6jIOz3Ebpql6y/wpWeSsSfX8qnk3I5nJ8Hod0wHJ1glCsIgqQ0R17sKy1caJXPr/32oS7/XUfXqM90Wk1zUwx++STnWeTddZnseCdL3Mzs2kS2JOAybdlvpWVvdJvfdBtWnaM5v2sfSke3e3cUtrgnSqLvsfMftx6HX/8cctrGNI997uZpaedLsbef1iPHVLfl2BkkfcG6yVPPIPLPl1hdfjVRqmqc4UsuJdU1mT6npUk5h/dO35LL1et+zSItYxXJ0gBIuoMURU++tv2ct0wffrzpzhJWJydYEgj+myJ52vNuSekC6e+SRFutQEniFCusOtvGoQvU7VRc6dgkE61col1fWotsEHgP3Mp2vSdZu5XPoh3aETvGoqvW7JrysMu8l6GiOqDdNi7DbEm/S9iY+1ucW8zYxaz/9XWVOM3YbRo4w9R6NHxbvW4619vjdIt/B8XthUr7vvikmsY7g6QQgWUWOIqD6Z3vS7StZ5SceNjocuig583tSupNtSz3tSJl0CMyGNdIlM9zLp3trFnB6s0x0/xdvISadacaKalJw1glnd0a+bkMbT63Uj43irIt1EZqdL0o1J4YVmSHeqq9enEUJ8crjDFaax0jDtVFcvDpj0dDnQcsb1Gv/8hPdZIp3qms709LGumQbAHWeGH0nHbRan18XYbaYTx0fExwy5JE5h+BE1hkiklePGM995vU5109y4m9dllHRb6njhJdI5mA3WSNd2mlnR0q/bcZhnSOl1Zk/JnrqMZJ5xRLrSIl6SsUuneNPOy0ljyfW6omxejgjpzsvltakn3fYDbSw96b79ynaWnnRWGqapvsbyNcY9OAjSqa5JdQDc1b98n6UnnZUBc1vqW9FpMvn2dE9fxA6AE6KHqDFEgt1fX9VIsNK+nPng69L1MctaSNfNDDuQjtmx3aUzc40TpOtlrl+vm56XxjqGdDbmRZAug+nhIF0zMymZdA3MlvN6HbezOunMSnddr9GvO9TG05PuIDOUQzorDdNUX6OT2cOGdKprUh0Ad5pZ3UU6KwPmIu3hSxB8ETRDZP/+/bjzzjtRWFiIpKQkTJw4EQ8//DB6ekIZnBhAOZkshfekTLpbZuex9KT7y7JLWHq9bloec+Bav065F4pa0QxUmznEMZNVSZdlklxI6HW/+MpM1jGkU535o2ooqN4DK4PECpnNqEinmig5gdmojHSqetX1WHkN1QZoqmtSvW8jmPlSpLPyuYjE4WaC4I2gGSK7d++Gw+HAs88+i08//RQ///nP8cwzz+B73/tesF7SENVGUWUzxrPOS7prpo9j6UlnpS236iC+Cwt4YQTSJTFdLqSLtfO+TEl33Qxe22DSFWbzkmf1upGJsaZJt9PyUjAy0WlA/iczUY9045j3gHSz8tNNvWU2DMwssjJI7MGyKaxjSLdoMq+sm3Q/v/kClp50qvqlpQWs90if4Kn6Gq9/cx5LT7qlpQWmHj+briOu6n1761uXsvSks/K5iMThZoLgjaAZImVlZXj++edx5ZVXoqioCEuWLMF3vvMdvPbaa8F6SUP6HBrW7DROWFuzs8FVNTPvnNGs85JubpH5EKv05DjMLXIOsVKd+goA4zN4Q+BIdy4zd4B0JWN5etLNYHYZJd20cTzDiHQjmU+NnrpvXnaOoV7//wmxvF8B0v3bLJ6BSroP97WY+o20fh1gbZDYzsNtrDWRrq2bGSLr1yXFxyDOpBQ8LsbmWpOqMchJqkxOcE+qVH0NantuBuli7DbTcvOkuIE1qd63cRlJiDd5T+NjbBiXkWTp/HQNkTbcTBC8EdIckfb2dmRk+La+u7u70dHR4fYnUKi2tVbtuMkZYrVSN8Sq+SQzd0Cnu6iA9+RCuqWlBawJwvRUd/el57HOT7prpo1l6Umn2qHz/LG8UJRe1+fQ8MCfdxrqH3h1p8vg/JCZqEe6i8/JYlUiXHyOc3z7a9sOs86v16kOEqus4yUlk061kmdLfSvO9hmbU2f7NLekRxVjcEt9K06bJVV2D06qXHPvfJ/GyLS8FKy5d77ba3AgHSfRs9Mj0VP1vn32xGKfxkh8jA2fPbHY7WdWBsxF2nAzQfBGyIbe7d27F7/61a/w05/+1Kdm5cqVePTRR4Py+qqJW6pJhqqcYG7Ket1nTbxOpp81ncKlk7MRH2vH5VOysaHGdxOuy6dku+aczJs0GrF2G3oNygVi7TbMm+T0Anl2qPUF6TKSmE+l/bqMEUy9TvfB3mbWpvbB3mbMP2+0W6t0I0gXY7fhZzdNN2x29983TXcZnGZrca3JQ6c2SExx8IpiZrVybxZmN1kaAOdPUuWae+fjVFcvvv3Kdhw8cQYT0pPw85svcHlCjI41eg2ra1IdAPfZE4txpPUMrv7l+zjd3YcRCTF461uXujwhnlgZMBdJw80EwRvKhsjy5cvx1FNPGWpqa2sxefJk17+PHDmCsrIyfPnLX0Z5ebnP41asWIH777/f9e+Ojg6MH89zhZsR7JbQql++6omhwCHmjBDS9Tk0VB8x9ipVH+lAn0NzfSmNTIw1rG4ZpfuCV+m4Of+80dh9jNceffexk5g/abRSyOHfLnR+TlQ8EPPPG422MzxDkqvzZHZBOt6uOcbSeUKDxMwoLcrCr9+tY+kAdSNb9XdBpWdP6cRMv5MqRybGYtXtxuPmQ9kSnnvfiHEZSfjkkTK2XvX8wMBwM0GIRJRDMw888ABqa2sN/xQVDbQMPnr0KL7whS/g4osvxnPPPWd47oSEBKSkpLj9CRSqiVucltDpupbQqg3TaFMwQ69TzeRXDUeptlRX7bipakgpPucDUPdAqDaWMjM4AfcOvbfOLWCdn6vzxtyJ5vlJaclxmNu/EalusqoVZ6rehFAkVVr5/ZdET0EIDcqGyOjRozF58mTDP/HxTlf5kSNHcNlll2HWrFl4/vnnYbeHr21JMBK39Bug6pev6uYBqOd8BNsdrWoYjWeWXI73o6xzVn4a6xjSdXYz+0v061SNux2H2ljn5+q8wclPelKXn6S6ycbYbSgZZ/xQUDIuxXV+VUMnFEmVqq8hiZ6CEDqCZhmQETJhwgT89Kc/xfHjx9HY2IjGxsZgvaQpKolbHO9Am847YOXLV2XzAID4WDvK5xcaHlM+v9CV8xFsd/TNsyew9KSbOJpXlUO6yTk8j5hedy6z5Jd0GnMjIZ1qtVOomkqVleTimVtnIifFPRE1JyUBz3h8tlU32Z5eB96p9Z1nBADv1Da52otb8SaEIqlS9TUiNdGzz6Ghsq4Fb+w4gsq6FnaekyBEKkFLVt2wYQP27t2LvXv3Ii/PvdmXxmwNHgy4iVtW3cuN7V1eQwo2OL/APL98n7l1Jh5Z8ykadSGCnJQEPLLkfK9fdCv6+wSsqnAfxmW3OY2QFV76CHDXpKp/5aOD3t6SQbzy0UHcOb8Ib+48wtK/ufMIFk7JVq6yAYA3tvNe443tztfoZk6xI10rM7+i1WJ+hT+oJCXSJvvomzVuHp6c1EQ8fG2x22dPpd35nfOLXIbO3au3wQZ3z6GRNyEUSZWqrxFpiZ7rqxsG3bNcL/dMEIYSQTNE7rjjDtxxxx3BOr1fcBK3rLqXQ/Hlu2JxMR64cjJerNyPA62dyM9IxtLSApcnRH+dKmtS1au2wVbN37Cyidce482nIV1PH29NpFOt5LFioBJ9Dk15A1RJSuR+9qy0YFcxdKyu3yqqrxEpiZ7rqxtw9+ptgz5Hje1duHv1NinHFYYsISvfHWpY9XCE6ss3xm5D8dhUZI1KQPaoRMMnOpU1qehVczhUK0is3INRCby28KTLSUlE9VHzap6c/hLknFRengvp9MadL7wZqOurG5Q8ZcHESq4OEHnehKEMJUl7+z3QMLgqTxCGEmKI+IA2EF/9IjSEz73s3KRq3PIVclIS8cgS78ZOsNzRS0sL8MS6WkO3vT559vaLC/HjdbsNq2Fs/TrAmpfpyvNzsPVgm8ErDOgAYE5RFv65+7ipfk5/9RIZR0YJq97yH6bmpXgdEz81L2XQPVtf3eD1c9fY0Y1lq7cNyvmwCtfNr3qf9USKN2Goo1oSLQhDiaiZvhvJqCSf0SblmTTZ2NGFZau3YX219zb2tCFcN2McSidmBsQwooZpRugbpsXH2nHXAuNk27sWFLqFmFQTBr9+ifH5PXW3XMRLuCUdGUdGiZiexlH5Cx95NUIA4JPDHSh/4SPXv/scGpa/tstwLctf2+V3giK5+T03N3Lz6z9H8bF2VtWMZ2hQCBwySVcYzohHxAeqDcoI1WQyFT1nk1rx2i6v7lmVfAPumqw0TLtgQjqAep965/+7o+LRibHbkBBrR3ev7yTUhFi761jVhFtaz2+ZScZnevoMO9sCwIaaJpzp6UNSfAyq6lpY1VpVdS245FxeLxpPVN38Pb0O1n3u6XWIMRIkZJKuMJyRbw0fqDYoA3w/ZTZ4eco00nt7KgWAqn3mm9SJzrOo2uc+e2R9dQPmPbURt6yqwn0v78Atq6ow76mNXr0nKmtS7anhz9M+16NTta/F0AgBgO5eh+s9spKIOYCv4tcBfrzOuPmZp65yXzNL70vH8a6pfrZVqmaE4CAN1oThjBgiPrA6X8PX97UG946bZk+lnnpAfbgZoGZYmK0JHmtS7amh8rRvlQ/qeBs56awkYtJ76nn9xzoGv6f1zTxDZ0CnOAhGB9fgVP1s+2esqSE9Mryj7/3iC6MGa2d6+vDQ67uw9Pcf4qHXd+GMSQXbweZOFD/0FgqXr0XxQ2/hIPNzLAhWEEPEB4GerwEMbqeuonei1vRc1dhRfVJW7anhz9M+d4M6zNwMSafarVbVWEuK4/2KkY6baOipUzE4VT/bVqtmVFHx3EUjZSW5uMLH9N0rirN9JjCXv/ARpvxwPV6sOoiKz5vxYtVBTPnherfcJD3nfG8tFvz0XXSedUAD0HnWgQU/fRfnfG9toC5FENwQQ8QHqq7Qxnbe3BXSNbTx9Hqd6nwaVWNH9UlZfTqutaf9YG5Qqt1qVY21K4vHsNZButkFGbCZvE02m1NHqBpHqp9tVWPNCqphymhk5boan/lGG2qasNJLGLD8hY8Mj/E0Rs753lr4imz2OiDGiBAUxBDxgWob7OZTvC6gpNt+6ARLr9fNLsww3cpt/TpAPXSSNTLBRAk3nWpPjTnM+LVep7pB5aXznsr1uhWLi/GNBYWDNlu7DfjGAvdutarGWl7GCN56+nVbD5yAWeNhTXPqCFXjSPWzrWqsqaJqSEUjPb0OrKrwneQNOLst9+isCJVEacAZjjFJr0KvAxKmEQKOGCIGqJSOtp3hGSKkszJZduuBE6bHaRjYpFRDJ6qLmpWfznpSnpWf3v93nkeEdFbyaOYW8UIbnroVi4ux+/Gr8dA1U3BbaT4eumYKdj9+tZsRAkTe5Fqrx6iWRasYa6pYSQyPNqwkDKsmSpf94n2WnqsTBC5SvmsCt3RUNehQmMl7UtbrVMM/aUm8LqOkaz7NM1xIt/XACdaX49YDJ1A6MVP5/CqhJcqZUDV29MTH2l0lur5Q7fYaY7dh9Kh4w+sYPSre9XnKGsH0Sul0Vo4B1BvdcUcLqCI9MsyxkjC8v4V3DOnOnOXNXeLqBIGLGCIMON0hS4uy8Ot360zPRfkbS0sL8KO1taZdRvVx99bTzCFw/bq2M8YVKgTpVJ/2VTcQ1fOrhpYAdWNKFdVur2d6+nw2MyM+Odzh6iNiKY3GeqGNcudTjrGmivTIMMdKwnBBZjIqPjc/piDTeUxSnB2dDCODm4AtCFzkExUg5k7MRHJ8jKFmRHwM5vZ/6cfYbab65IQYt6fTDGYOB+lU9apJjFbCFGnJxl6atOQ41/mbTzKNCp3On02tp9eB31fsww/fqMbvK/a5xdv1qIQ1frT2U9Z6SNfMDKfpdVaOISKhXFZ6ZJhjJWH4e8xwGenW33cpS8/VCQIX8Ygw6Ol1sNzR8bF2dBrU58fpjtlS32o6jfZ0d59b2IEGr5lBuixmVQvpVJ/2Z4xPY52fq9O/DgCc6ORtsHod5a2YzUWhvBVi5boarKqodzvuiXW1KJ/vPf+BG9bYeaiNdQ2ks2JIWTW+1lc34Pt/3YGWMwMGV2aSHU/cOMNnKeiZnj78eF0N9rd0oiAzGd9bXOz05PiB1cnVhJUpxUMNShh+dpPvhFXPhOGk+BgsKs42TFhdVJztun8TspIRa4dhwmqs3akThEAiHhETVq6rweSH3sLja2vxQuUBPL62FpMfemtQqdyW+lZWsy6rpbKAenLo1oO8xmB6HT3tj0lx96aMSUkY9LT/pw8PsM5POs57dEL3HtltvI+nXqeSt0KsXFeDZzfVDzrOoQHPbqr3WhYJcLu9qsVNVJNbrR5DM4v0RggAtJxx+JxZpNqPQoWyklzctaBwUOmyzeacP+TLMIqm3iNWEoZX3TYbi3z0HllUnI1Vt812+9neH18DXyk/sXbn/wtCoBFDxACVDUrVsFAtlQXUN9n/3Wxc7kd415m3L1dNoFN9j6w091J9DStlkQQnrDGPOQ+GdJTcaoQ+uZWOWTLdeBrvkum5rmP6HBru9jFVmrh79Ta361HtR6HK+uoGPOfjd+25TfV+jyMYLnCru/Ssum02ah8rw9K5EzD/3CwsnTsBtY+VDTJCiL0/vgabvvMFJMfZYQOQHGfHpu98QYwQIWhIaMYH3A3qgSsnIz7Wruwed/TxYvF6nWrVzJmzvNfQ6+jL3fNIal+u94qoJtCpvkdzizJZA+z0pbiqr6FSFqlP0uQOBiwtysQz7+8zXU9p/zUoJ7fCaVis2Wm86a7Z2YD/KpuCGLsNG3c1ssrAN+5qxKLpucqD+1QxG48ADB4wqTq4bzhhJWE4KT4Gj18/la2fkJWMmsevVl2aIFhCPCI+UK3bV024+3A/L2yi16lWzWSYJIYSpFNtLKWaQKf6HvU5NJ8Jo0RPr8PtyX3quFTjBXno6ltOs/R6ncqT+GfHTrLOTzrV3g+AegfdHzATaElnZU0qWOkjIr1HBGH4IIaID1TDDqrdKq3UXKpWwTx+He8JiHSqX+6qHTdV36M/frCf9eT+xw/2u/791PpakyPcdap3QdVYO3SC58UinWrvB0A9HHWqu5elJ52VNakQqiZugiBEJmKI+MBK3b5KWaeV/AfVqpnLi8ewPBaX9885sfLlrppAp/IefbSf9zSr16lumheMTzdRwk2naqypfo6op4MZep1qOGqsSWIrQTorayI4eTShrBQSBCHykBwRH3x1Tj4eX2v+dP3VOflu/+aWdc4tykRacpxhFUl6cpxb/gOFNow2wlyPrp6ckj9am9Uvd9WOm9z3yKzPijedahOn3DTevBzSqRprS0sL8MS6WtNyYgpffW9xMV6sOmh6fn2PiFn56bDZYDijxqarpnrlrosx80cbTF/jlbsuBgA8WDaFtaYHy6a4/ZubR6P6ubZ6jCAIkYl4RHywg9n/wZuOU9YZY7fhyRuMQycrb5g6qDri4WuLYYP30IYN7qENbhIjPaWqlgfroQS6x64rwZ3zi0zbfnPeoxsvyDNejBedahMn1dJXVWNNNXyVFB+DaXkphvppeSluSaEf7W9lDcojz1HGyHjWfc4Y6aze2XWk3Vjcj16nkkejWvVDx5SMM36fSsalDLtEVUEYjogh4oNQxKDLSnLxzK0zB4VcclMT8YyXwWN0DDe0oZrEaKUHRzCZwwxf6XXUxMkIfRMnVePOShfQFYuLDXs56MNXfQ4Nx08aJyUfP9njFuL4oK7ZUO+pa+88y7rP7f3eut2NvPtNOtU8mj6HZupxebHqoNs19/Q68E6tcSXPO7VNpsnOgiCEHzFEfBCqGHRZSS7+tXwhXiqfi198ZQZeKp+LzQ8u9NnAiY7Z/KD5MarG1NETvPwKrs5fuAaPp27VbbN9ehWm5aUM6p+gYtyR4eJrH9cwuAvo+uoG/NNH+es/a5rcvAOqxiMAHGEmxJLu3/+whaUn3dPrGbEunU41j+aDz5sNOxIDQGdPHz74fMDgsjKNVhCEyERyRHygOmXVH/ocGmqOtrvyK2blp5u6lDnDylSNqR2H21j6HYfbcOOF41laf7Ay9A5wbvy7fPTi2HW4A+urGwYZbaqTaLmo9siw4okbm8ZMPu3XHTUxdAjSdTG9CqRTvYa/bj/M0v91+2HMnzQagLVptIIgRCbiEfGBejmuNbgt5K2gOmTOLM+A4Or8pZU5zE2v42783qo3OHkrdH5fUCMtOr+qdyBrBLPjrk43J58ZwurXqVbNmOX7EKRTNYDNvCGEXmelqk0QhMhEDBEDVFz2VrA64ySQuG+1XAsjNJZIBnNon17nT6MrzvRd1fMrezjU28vgs+OnWIeQzldrb09I99Prp7H0pFPNo5ldwPMq6nVWptEKghCZSGjGhGC57FVbyFtBZchc6cRMjErkdWLl6ozgTEzNSeWV1up1VpOMudN3Vc+v6h1oZnqB9LpDzJwd0u1hdnvdc+wkSkdm4uoLxgKv7jDVX33BWADq03Rvv7gAP36r1rT8+PaLC1z/tjKNVhCEyER+SxnwpqyqEYpkO9VNM8bO+zhwdb7gTky1MlXWSpKximdK9fyq3gEr61cNU6h/Lmx45taZhtpnbp3p9nuh4k2Mj7XjLpMS57u8GBVWptEKghB5iCESJkKRbKe6qVnp9qqKan8JytPxhWeejurGrzp9V/X8qrlGnLyedF1eD6AeprBi7FCpeUaS+1dGZlKMYak5p7oLsG5UWJlGKwhCZCGhmTARimQ71e6TVrq9qhCKiamqYQHV6buq5wcGvAOeXUZzvHQZ5eC5XNUwhdWKMCthSk51F6HaoZewMo1WEITIQTwiYSIUyXaqHSutdHtVQTXRU7VChVAJC1jxTFlJYuZ6Bzh5PW39eT16VDwK/lSEBSNMqUe1Q68gCEMf8YiEiVAk23FbvP9X2RTXhkIu+EfW1Lj15/A2I0QV1dwEFcPF86mb+/Ru1TMVLO+APx19VTwKgfbSCIIgWEUMkTBCT6me1Rp2GwZVa1hBpUunfoMMVqWQao8Mf9vsczZ+1aF0qudXxd+OviphimDdZ0EQBBXEEAkzVuPiHPzZyIOxyar2yAhFm/1IKwP1t6MvpyxaT1DusyAIggJiiEQAwUq2C9W8HC6qPTJC1WbfqmdKddPnYCUZllhf3TAopJaTkohHlkioRRCEyEUMkWFMKOfl9PQ6TL06qoaRP5uyKqqeqfXVDYPyKwKRRwNYy99YX92AZau3Dfp5Y0cXlq3e5rPEVhAEIdzYNC1Uk0PU6ejoQGpqKtrb25GS4n2aqhWC8SQbqVDfDsD7Ru6rykPlPfLWldSbN6HPoWHeUxtNDaPNDy50ey3u+UMFvaee12D2nqrCvQd9Dg2zfrTBtOz64x8sGnR8NP0uCIIQOlT276jziATzSTYSsfp0zX2PqCupJ9SVFBgIfVjxcKyvbsBzm+oHbfoODXhuUz0umJAesPvG2ZRD0QuF4OZvVO1rYbXyr9rXgkvOyXL9zBnK+RSNHQMhs5yUBDyy5Pxh+bsgCEJkElUekVA9yUYi3Cdflfeop9eByQ+9ZVpxsvvxq91CHFxDhzwoZg3ZPD0oVuCuqbKuBbesqjI930vlc0OWBPrTf+zBr9/da6q79wvn4DtXTQLgO5RDSChHEAR/EI+IF0L5JBuJcJ6uVd8j1a6kBLds1Gr5sSq+jK+G/rbzeuPL35Li4KA2NbnPoWH5a7sMlctf2zVsfxcEQYgsoqZtoT/j4aMF1ffIn3k5nA6dje1nWOfn6rxhZHwBzmvWd2+NtEokACgtyjIX6XRVdeahnLbOs6iqa/F7bYIgCGZEjSESmU+ykYXqexTseTmtp3sCqvOGitcFUB96FwrmTsxkzWOZ2+81qtzXzDovVycIguAPUWOIROKTbKSh+h4Fe15OxkheJ1auzhuqXhd/5rQEiz6HhrN9DkPN2T6HbiaPYmc5QRCEIBI1hkgkPslGGqrvEXUlNcKfrqQ5KTzDiKvzhhWvi5Whd4DTYKisa8EbO46gsq5l0LA+q7xYuR9mKedaf64OAHY+jXRcFQQhFERNsmoom2MNVay8R8Gcl0OGkVnVjD/GY3pyvCWd6pyWYJaNq+bqzC3KRFpynGnfkblFYogIghB8osYjAlh/ko0mrLxHKxYXY/fjV+Oha6bgttJ8PHTNFOx+/Gq/m42RYWSD9zCIDf4bjyc6eR4RbzpOwi0wUJXjaVA19lflrK82npBshmquTozdhidvmGqoXXnD1Kg2ygVBCB1R1UeEkG6S5kTSexRMb8Lfth3Gt/+801T385um40sz85TPb9YLxVc3WRX86efiOZtmODf3EwQhdEgfERNk4qg5qu9RMA2XYI6rz0lNCqjOE5WSaKufSasThMtKcrFw8pigTH4WBEHgEpWGSLCJJG9CKAhF2/xgGY/BzkMJVdm4lVwdb/ftd5vrxSMiCEJIicrQTDCJtlk2w6Ftvq9rAJzX4c81hLolPGcKMjA87psgCJGLyv4tPtgAEuykxEjDrCU84N6VNFKhBN1cjwTd3AAkMYe6bDw+1o475xfhsetKcOf8Iq9GyHC5b4IgDA8kNBMgQj3LJhLCP6HIfwgVwcpDicSy8eF03wRBGPqIIRIgQvnlHinhn+HWNj9YeSjkcfG8ZzlhCtkNt/smCMLQRgyRABGqL3dfsf1GL5Nig420zecTzMofVeS+CYIQSYghEiBC8eUe6vCPGZT/0Nje5TPRM8dH/kMoQkuREL7SEyll4/7cN0EQhEAjhkiACMWXeyjDP5xN3Gr+g7OR1qdo7Oh2/SwnJQGPLDk/YN6cSAlfRSKRmLciCEL0IlUzASIUU1lDGf6Z99RG3LKqCve9vAO3rKrCvKc2eq36UW0Jv766ActWb3MzQgCgsaMbywJUWRRt1UtWkHEHgiBECtJHJMAE80k8FD0prPaX4HhQ+hwaZv1og+GwtbTkOGz9wSLLBlsoWqoPJyItfCUIwvBAWryHkWAmJQY7/ONPDgon/6GqrsXQCAGAts6zqKprwSXnZqktvh8pTVUjUvJWBEGIXiQ0EwS4U1mtnDeY4R+VTdwKlfuaA6rzhpSmCoIgDC3EEBliBDO2H/xNnGsgWTfcpDRVEARhaCGhmSFIsMI//mzinFyD0omZ+PW7e03P70+oINhD7ARBEITAIobIECUYsX2rOSjcBN25RZlIS44zzBNJT47D3CLr1xVjt2HJ9Fw8u6nep2bJ9FxJyBQEQYgQJDQjuLCSg6JSKhtjt+HJG6YarmHlDVP9MhL6HBrW7DQuz12zs0EGugmCIEQIYogIbqjkoFiZ4lpWkotnbp2JnJTBk26fCUD/CrOEW8C/hFtBEAQhsIQkNNPd3Y05c+Zg586d2L59O2bMmBGKlxUsws1BsVoqG8wSZ6maEQRBGFqExBD5r//6L4wdOxY7d+4MxcsJAYCTg+LPph+s/hVSNSMIgjC0CHpo5q233sLbb7+Nn/70p8F+KSHEROKmTwm3vnwrNkjVjCAIQiQRVEPk2LFjKC8vx4svvojk5GRTfXd3Nzo6Otz+CJFLJG76oZj5IwiCIASOoBkimqbhjjvuwLJly3DhhReyjlm5ciVSU1Ndf8aPHx+s5UUdfQ4NlXUteGPHEVTWtZhWjXD0kbrpy0A3QRCEoYPy0Lvly5fjqaeeMtTU1tbi7bffxp///Ge8//77iImJwf79+1FYWGiYrNrd3Y3u7oGprB0dHRg/fvyQGnoXiagO4gu2PlTIQDdBEITwoDL0TtkQOX78OFpaWgw1RUVFuOmmm/Dmm2/CZhv44u/r60NMTAy+9rWv4Y9//KPpaw3F6buRhuo03WBO3xUEQRCig6AaIlwOHjzoluNx9OhRXHXVVfjLX/6COXPmIC8vz/QcYoj4R59Dw7ynNvossaVOqZsfXIgYu01ZLwiCIAjeUNm/g1a+O2HCBLd/jxw5EgAwceJElhEi+I9qnw+rfUEEQRAEwSrSWXUYo9rnQ5qBCYIgCKEmZEPvCgoKEKQoUFTCyclQ7fMRiX1BBEEQhOGNTN8dgnCrVFSn6VqdvisIgiAIVpHQzBBDddqtSp+PSO0LYhXV3imCIAhC6Ala1UwgkKoZd6xWtayvbsAjaz5FY8dAj5aclAQ8suT8sPUF6el14MXK/TjQ2on8jGQsLS1AfKxvu1i1PDhSe5sIgiBEAxFRNSMEHv+qWnz5OAYTzOm4ALByXQ1WVdRD76B4Yl0tyucXYsXi4kF6Kw3WvPVCIa+RdFcVBEGIHCQ0M4SwUtVCm3Jjh/uxxzoGh3L00HTc62aMQ+nEzIAaIc9ucjdCAMChAc9uqsfKdTVuP1cJRQFOz8mjb9Z4zXGhnz36Zo2EaQRBECIEMUSGEKpVLZG2Kff0OrCqot5Qs6qiHj29DgDW1q/iNfJEckoEQRBCj4RmhhCqVS2R1qDsxcr9gzwhnjg0p+7O+UWW1m+1F4rklAiCIIQH8YgMIVSrWiKtQdmB1k4lnZX1W+mFohr+EQRBEAKHGCJDDJUR95HWoCw/I1lJZ2X95DXyldFig9PTQV6jSAtfCYIgRBsSmhmCcKtaaFM2Cm/k+mhQFoxpuktLC/DEulrD8Izd5tTp16/SYI28Rnev3gYb4HacN69RpIWvBEEQog0xRIYoVNViplkyPRfPbvKdILpkeu4gAyNY+RLxsXaUzy80XE/5/EJXPxFVo4Igr5HnNeR4uYZIC18JgiBEG2KIDGP6HBrW7DTOb1izswH/VTbFtZkHuwcH9Qnx7CNit8FrHxEVo8LzOI7XKNLCV4IgCNGGGCIRQDDCIIB52AFwDzuY5UvY4MyXWFSc49f6ViwuxgNXTmZ3Vi0rycXCyWOUOrECPK+RzNcRBEEIL2KIhBnV9usEx3hRDTv4ky8RLGMK8B4q+t3metNQEWdNVsM/giAIQmAQQySMrK9uwLLV2wb9vLGjG8tWb8MzPsIg3BwO1bBDqHpwqLR4txoqUlmT1fCPIAiC4D9iiDAIxtN+n0PD8td2GWqWv7ZrUBhEZWOeOi6VtRbS+dODg2soUIt3T6jFOzCQR2I1VGTFeAn2fB1BEATBO9JHxIT11Q2Y99RG3LKqCve9vAO3rKrCvKc2+t3kqqquBW2dZw01bZ1nUVXX4vq3as+LJ9bWeFEOhnQzxqex9KQzW4/msR7VFu9W2rVLXxBBEIShhRgiBgSz42blvmZlnerG/O6eJtZrkG511X6WnnQqybCAWot3wFqoyOqsGSsGp8ymEQRB8B8Jzfgg+BUk3GMGdMHuefHR/hNsXfkCDJro6wvSqbZ4txIq8mdCsUooR2bTCIIgBAbxiPgg2FNcuV069TrVjfnS87JYetIlx8ew9KQ7zjRESDc+PYmlJ51qu3YgNBOKZTaNIAhC4BBDxAf+VJBwXPxzizKRlhxneO705DjMLRowRFQ35sVTx7KugXTFOSksPek+PdrO0pNuMvP8pFMd8geov0eqBqfkoAiCIAQWMUR8EOwprjF2G568YarhuVfeMNVtk1XdmFtNkmEJ0o1OSWDpSXekjWeska61s4e5ngEdldZmj4p302SPivcaMgn2hGJ/PGWCIAjCYMQQ8UEopriWleTimVtnIifF3ejJTU302UNEZfpu88luz8O9QrqcVF7ohHR5aTxjjXRW26lvP3gCTSfdjZimkz3YftB7TktZSS7uWlAIm8fNs9mAuxYUhqXXiiAIguAdSVb1QaimuFrpX8E95gTTA0E61Wm9N84aj9dNZtmQTn9+lXbqvvqOaBjcd4RYX91g2KvkggnpLmNE9ZplNo0gCEJgEY+IASreB3+elGkmynUzxqF0YiarCodzDLeYh3Q0rdcI/bTei8/JQoLJvJeEWDsuPifLdf6Hry32aoQATuNCb9z19DrwnMGkXgB4btNA3xGA1yhuxWu7XJ4p1Wu2kkArCIIg+EYMERPKSnLx/ne/gIeumYLbSvPx0DVT8P53vzAobBKJT8qlRbyqGdJxp/Xqw0tJJpU23Eocb/zxg3qfRguh9euIqn3mjeJOdJ5F1T5nozjVa7aSQEtI3xFBEITBSGjGBO7ANX+muAZrYNzcic7KHKONOS05DnP7Q0Wq03q31LeyNn3P6b5G6HuzqPY1AYBKXSdaIyrrWnDJOVnK1wwM5KCsqqiHprvZNhtQPr/Q79k3giAI0YR4RAxQrYKx8qQcrBbytCazypwndZU5quGlQFecAO4VJyOY3hR3HdfLoLmtzQzPBmjPbaof1CXWoTlDRZ73TvqOCIIg+EYMER9YrYL57a0zMcajDHZMSoLP7pzB3qCoMmeMR/nrmFHxgypzVMNLqvrG9jMsPelumJnH0ut1cwp5jeJIF8gGaIT+cyF9RwRBEIwRQ8QH/vWL8OUTGUB1YJz/mK9pVn66aYKr3ebUAeqJm62nmX1E+nVzijJNG+Hb+nWEg/l+kW5WfvqgMt9Br6G7ZtXPhfQdEQRBMEYMER9YddkvW71t0AyWxo4uLPPwcKiGKTzhJj7Smo559BQ5drJ70Jq2HjjBGkq39YAzd0O1CiZjJK9hGum2HjjBSlal9QDA6zuOsF6DdB/tb3XL8/D6GppTBwQ/fCUIghBtSLKqD6y47Dllo5SIqTowTg838ZGzpuUBWhMHz8ZtZjorm/jpnj7WMaRTTW4NdvhKEAQh2hCPiA8o7GCEPuygWjbaeorX9dRTp5JXUlVnvqa2zrOoqrO2JrMqGJpQTN6aGePTWOcnXdYIngdFr5tdkM46ZkCnltyqGo6SviOCIAjGiCHiA9VGVypP1gCQkshzRul1qomPlfuaeWvq12WMiDdRwk2nmv/wpw8PsM7v0nErmHW62y8uZOWV3H5xIQD1Xiuq1VH+9B0RBEGIBsQQ8YFqo6vu3l7WeUm3/VAbS6/XqSc+qu3k2czQSbbF0MmB1k6WnnTNTA+NXhcfa8ddCwoN9XctKER8f0dY6rVihL7XCqA2y4b03A69giAI0YbkiPhAtdHVe7ubWOd9b3cTvn/N+djdeJKl1+tUN/7SiZn49bt7TfWu2TdqUQrl0El+RjJLTzqr+RU0e+a5Te6dWW1wGgr62TTUa2XZ6m0+z/+kxxRk6iPi+XZRHxH9LBvCykwhQRCEaEAMER+o9rw42c1LkiRdQxvPO6DXjYzhNfgi3dwi886q6clxmNtf/tp8mumBIJ1i6OSrc/Lx+NpaU/lX5+QD8K9b7YrFxXjgysl4sXI/DrR2Ij8jGUtLC1yeED3Ua+WRNZ+isWPgPchJScAjS84flADM6SNCCcB6aD6QIAiCMIAYIj5Q7XkxLi3JbRPzxbi0JABA2xnjJFJCr/vNJnPvBukun5rDetpfqXvaV/VAqIZOdjDDUTsOtbkG+alMQPYkPtaOO+cXsV6T67GwOmVZEARB8I7kiPhAtefF/95xEUtPulg7763X6+qbT7OO0eu2HzSe16L/f9UKD1XDxUo5bijzKzgTjaUviCAIQmARj4gPVHtepCbHIT8zCQdafId08jOTkNqfGFmYlYxPjpjniRRmDeRV9PTywj+k6+l1YFVFvaF2VUU9HrhyMuJj7coeCNVyXKs5H5GUXyF9QQRBEAKLeER8oNpHBABWXD3FUK///4xkZqmsTsfdeEn3YuV+VqfUFyv3u/6t4oFQLccdDj01/LkGbjfcUBKJaxIEIboQj4gP9N4BwNw70OfQ8K2Xdxie81sv70DtY87cjYaTvBwUvU5jZoeSTrVcluB6IFTPbzXng9tJNhTQNfjKu/Fsa09E0jVE8poEQYg+xCNigMo03c17jqOn12F4vp5eBzbvOQ4AGJfKy0HR61TDRarlsno4+RLj03nn1+tUcz5CMaE42ETiNUTimgRBiE7EEGFhPrn2uc37WGci3RhmDoFe96ULxrGOId3S0gLWNN2lpQWs83oyecwoS7qyklxsfnAhXiqfi198ZQZeKp+LzQ8uHGSEqHaSDQWqbe0j+RoiaU2CIEQvYogYQE+NnkPejnUMfmrs6OKV45LusEmzNEKvmzoujXUM6eJj7Sifb9xltHx+odfeGhxazzBLnL3oOB4X9U6ywUd1TcPhGgRBEIKJGCI+UH1qnJ6Xxjov6QoyeWENvc7Kxn/BBOMhcGb/b0SwK0gisVRWdU3D4RoEQRCCiRgiPlB9avz+NcU+tXpI973FPL1ep7rx9zk0LH9tl6F2xWu7vLrgOdUUwa6CicRSWdU1DYdrEARBCCZiiPhA9akxKT4Gi4qzDbWLirORFB/j0udnJhnq8zOTXHpAfeOv2tdi2N4dAE50nkXVPvfJweurGzDvqY24ZVUV7nt5B25ZVYV5T20clMBIFSS+Mgl8VZBwicRyX9U1DYdrEARBCCZiiPjAylPjqttm+zRGFhVnY9Vts13/7ul14FCr8TybQ61n3CpxVEfKV9a1gINeF0nVFKrXG4lrGg7XIAiCEEzEEPGB1afGVbfNRu1jZVg6dwLmn5uFpXMnoPaxMjcjBLDWbAxQLX9VG6ermhejWkFihVC2eA/WmobDNQiCIAQLaWjmA38GrsXH2rF46lhXMzBvVSlWm40B/IZjpUVZ+PW7daavUVqUBUB9oFuoBsBFUot3q2saDtcgCIIQDMQQMYCeGj27T+YYdJ/kdqv0p9kY4PRG1Bxtd424n5WfPmgDmTsxE8nxMejs8T2jZkR8DOb2GwmRXBFC5b6RhOqahsM1CIIgBBoxRExQeWqk/ArPQATlV+hd3ktLC/DEulrD8IyvZmMr19VgVUW927FPrKtF+fxCrPCoxomPtRsaInE6b00Wc+Iw6aT6QhAEQfAXyRFhwGm+pZpfYbXZ2Mp1NXh2U/0gA8ahAc9uqsfKdQM5G1vqW02rZto6zw40rlJLKZHqC0EQBMFvxBAJEFa6Va5YXIxvLPBujHxjwWDvRk+vA6sq6g3Xsaqi3lVpoxo6aT7dzdKTTqovBEEQBH8RQyRAWM2XeHaTd8PC289VK21C0XxLqi8EQRAEf5AckQBhZRMvWL7WUFuwfC32P3mN69+qlTYUOmls7/IadbHBaTB4Nt8y8ux4C7VI9YUgCIJgFfGIBAjVfIk9R0+yzqvXjUmJZx1DOivNt5ZMN/ZgLJmeKwaGIAiCEDDEEAkQqpv+1b/cxDqvXrf9QBvrGL1OJXTS59CwZqdx59Q1OxsGNSjjtoQXBEEQBE8kNBNAVPqOOLydwAt63aETvDwUTx03dGKWcAsMblCmUrIsCIIgCJ6IIRJggpkvMTIhxlzkQ8dpXKWacGtWskwt3hcV50g4RxAEQfCKhGaCAKfvyJKS0axz6XU3XZjHOoar80Q14dZKybIgCIIg6BFDJEw8ddMsZd2EzJGsY7g6Ty4qzEBacpyhJi05zpVwG8oW74IgCMLwRAyRMJEUH4NFxdmGmkXF2UiKHwizUGWOEb46mfY5NFTWteCNHUdQWddieSKu3rcjLd4FQRAEfxFDJIysum22T2NkUXE2Vt022+1nVJljVCLsrZMpt6qF0xL+hK4lvLR4FwRBEPxFDJEws+q22ah9rAxL507A/HOzsHTuBNQ+VjbICCGoMsfTM5Lro5MpVbV45nJQVYveGFENtUiLd0EQBMFfglo1s3btWjz22GP45JNPkJiYiEsvvRSvv/56MF9ySJIUH4PHr5/K1nMrc1SrWvxp8c4pWRYEQRAET4JmiPz1r39FeXk5fvzjH2PhwoXo7e1FdXV1sF4u6uCU46pUtZROzJQW74IgCELICYoh0tvbi/vuuw9PP/007rzzTtfPi4uLDY6KXvocWlA2cSuhlpJxKYaGSMm4FK9r4xhGgiAIguBJUAyRbdu24ciRI7Db7bjgggvQ2NiIGTNm4Omnn0ZJSUkwXnLIsr66YVBYIzdAYQ3VUEtPrwPv1DYZat+pbUJPrwPxsZJeJAiCIPhPUHaTffv2AQAeeeQR/OAHP8Df//53pKen47LLLkNrq+/mVt3d3ejo6HD7M5xRSSS1gmpVy4uV+2FW1evQnDpBEARBCARKhsjy5cths9kM/+zevRsOh3NCyve//33ceOONmDVrFp5//nnYbDa8+uqrPs+/cuVKpKamuv6MHz/ev6uLYMwSSQFnIqnVfh+AelXLgdZO1nm5OkEQBEEwQyk088ADD+COO+4w1BQVFaGhwfkkr88JSUhIQFFREQ4ePOjz2BUrVuD+++93/bujo2PYGiOqiaRWUalqyc9IZp2TqxMEQRAEM5QMkdGjR2P0aPMZKbNmzUJCQgL27NmDefPmAQDOnj2L/fv3Iz8/3+dxCQkJSEhIUFnSkCWU7dG5VS1LSwvwxLpaw/CM3ebUCYIgCEIgCEqyakpKCpYtW4aHH34Y48ePR35+Pp5++mkAwJe//OVgvOSQI9Tt0TlVLfGxdpTPL8Szm+p9asrnF0qiqiAIghAwgtZH5Omnn0ZsbCyWLl2KM2fOYM6cOdi4cSPS09OD9ZJDCkokbWzv8ponYoMzfBLq9ugrFjvDaasq6t08I3ab0wih/xcEQRCEQGDTNM16NmSQ6ejoQGpqKtrb25GSkhLu5QQcqpoB4GaMUMDEW8v2UHGqqxfffmU7Dp44gwnpSfj5zRdgZGJQG/EKgiAIwwSV/VsMkTATzD4iVlm5rkY8IoIgCIJlVPZvecQNM5HWHn3luhqvOSIODa6fizEiCIIgBAoxRCKASGmP3tPrwKoK34mqgDN35IErJ0vCqiAIghAQZDcRXEhnVUEQBCHUiCEiuJDOqoIgCEKoEUNEcCGdVQVBEIRQI4aI4GJpaQHMcmSls6ogCIIQSMQQGaL0OTRU1rXgjR1HUFnX4tdwPII6qxohnVUFQRCEQCJVM0OQYPYekc6qgiAIQiiRhmYM+hxaxPT5oG6snjct0N1Ye3odeLFyPw60diI/IxlLSwvEEyIIgiCwkIZmASSSOp/2OTQ8+maN19k0GpzGyKNv1mBRcY7fhlJ8rB13zi/y6xyCIAiCYIY84hpA3ge9EQIAje1duHv1NqyvbgjperbUtw5aix4NQEN7F7bUt4ZuUYIgCILgB2KI+MDM+wA4vQ+BSBLl0nTStxFiRScIgiAI4UYMER9Eovche1RiQHWCIAiCEG7EEPFBJHofLirMQG5qInxlf9jgzF+5qDAjZGsSBEEQBH8QQ8QHkeh9iLHb8PC1zvJZT2OE/v3wtcVhq+gRBEEQBFXEEPFBpHofykpy8dtbZyIn1d0AyklNDFjpriAIgiCECinf9QF5H+5evQ02wC1pNdzeh7KSXCwqzomY3iaCIAiCYBVpaGZCJPUREQRBEIShgDQ0CyDifRAEQRCE4CGGCIMYuw2lEzPDvYxhRSS1zRcEQRDChxgiQsiRcJcgCIJASNWMEFIirW2+IAiCEF7EEBFCRiS2zRcEQRDCixgiQsiIxLb5giAIQngRQ0QIGZHYNl8QBEEIL2KICCEjEtvmC4IgCOFFDBEhZERq23xBEAQhfIghIoQMGdonCIIgeCKGiBBSZGifIAiCoEcamgkhR9rmC4IgCIQYIkJYkLb5giAIAiChGUEQBEEQwogYIoIgCIIghA0xRARBEARBCBuSIzJE6XNokuwpCIIgDHnEEBmCrK9uwKNv1rjNbclNTcTD1xZL+asgCIIwpJDQzBBjfXUD7l69bdDwuMb2Lty9ehvWVzeEaWWCIAiCoI4YIkOIPoeGR9+sgebl/+hnj75Zgz6HN4UgCIIgRB5iiAwhttS3DvKE6NEANLR3YUt9a+gW1U+fQ0NlXQve2HEElXUtYgwJgiAILCRHZAjRdNK3EWJFFygkZ0UQBEGwinhEhhDZoxLNRQq6QCA5K4IgCII/iCEyhLioMAO5qYmDJtcSNjg9ERcVZvj9WpxQi+SsCIIgCP4ioZkhRIzdhoevLcbdq7fBBrgZAGScPHxtsd/9RLihFpWcFZkrIwiCIHhDPCJDjLKSXPz21pnISXUPv+SkJuK3t870OydDJdQSqTkrgiAIwtBBPCJDkLKSXCwqzgl4Z1WzUIsNzlDLouIcxNhtEZmzIgiCIAwtxBAZosTYbQEPd6iGWihnpbG9y6vxYoPTUxOInBVBEARheCKhGcGFaqiFclYADEqgDWTOiiAIgjB8EUNEcGEl1BLsnBVBEARheCOhGcGF1VBLsHJWBEEQhOGPGCKCCwq1LFu9zev/a/AdaglGzoogCIIw/JHQjCAIgiAIYUMMEcEFle/6gsp3pVOqIAiCECjEEBFcRPJ0X0EQBGF4IoaI4EI6pQqCIAihRgwRwYV0ShUEQRBCjRgigotQTvcVBEEQBEAMEUGHdEoVBEEQQo0YIoIb0ilVEARBCCXS0EwYhHRKFQRBEEKFGCKCV6RTqiAIghAKJDQjCIIgCELYEENEEARBEISwIYaIIAiCIAhhQwwRQRAEQRDChhgigiAIgiCEDTFEBEEQBEEIG0EzRD777DNcd911yMrKQkpKCubNm4d33303WC8nCIIgCMIQJGiGyBe/+EX09vZi48aN2Lp1K6ZPn44vfvGLaGxsDNZLCoIgCIIwxAiKIdLc3IzPP/8cy5cvx7Rp03DuuefiySefRGdnJ6qrq4PxkoIgCIIgDEGCYohkZmZi0qRJeOGFF3D69Gn09vbi2WefRXZ2NmbNmuXzuO7ubnR0dLj9EQRBEARh+BKUFu82mw3//Oc/cf3112PUqFGw2+3Izs7G+vXrkZ6e7vO4lStX4tFHHw3GkgRBEARBiECUPCLLly+HzWYz/LN7925omoZ77rkH2dnZqKiowJYtW3D99dfj2muvRUNDg8/zr1ixAu3t7a4/hw4d8vsCBUEQBEGIXGyapmlc8fHjx9HS0mKoKSoqQkVFBa688kqcOHECKSkprv8799xzceedd2L58uWs1+vo6EBqaira29vdziMEn55eB16s3I8DrZ3Iz0jG0tICxMdKtbcgCIJgjsr+rRSaGT16NEaPHm2q6+zsBADY7e4bl91uh8PhUHlJIQysXFeDVRX1cOhM1CfW1aJ8fiFWLC4O38IEQRCEYUdQHnFLS0uRnp6O22+/HTt37sRnn32G7373u6ivr8c111wTjJcUAsTKdTV4dpO7EQIADg14dlM9Vq6rCc/CBEEQhGFJUAyRrKwsrF+/HqdOncLChQtx4YUXYvPmzXjjjTcwffr0YLykEAB6eh1YVVFvqFlVUY+eXvFqCYIgCIEhKFUzAHDhhRfiH//4R7BOLwSBFyv3D/KEeOLQnLo75xeFZlGCIAjCsEayDwUXB1o7A6oTBEEQBDPEEBFc5GckB1QnCIIgCGaIISK4WFpaALvNWGO3OXWCIAiCEAjEEBFcxMfaUT6/0FBTPr9Q+okIgiAIASNoyarC0IT6hHj2EbHbIH1EBEEQhICj1Fk11Ehn1fAhnVUFQRAEqwSts6oQPcTH2qVEVxAEQQg68ogrCIIgCELYEENEEARBEISwIYaIIAiCIAhhQwwRQRAEQRDChhgigiAIgiCEDTFEBEEQBEEIG2KICIIgCIIQNsQQEQRBEAQhbIghIgiCIAhC2IjozqrUfb6joyPMKxEEQRAEgQvt25wpMhFtiJw8eRIAMH78+DCvRBAEQRAEVU6ePInU1FRDTUQPvXM4HDh69ChGjRoFm80W0HN3dHRg/PjxOHToUNQM1JNrlmserkTbNUfb9QJyzUPtmjVNw8mTJzF27FjY7cZZIBHtEbHb7cjLywvqa6SkpAy5G+wvcs3RgVzz8CfarheQax5KmHlCCElWFQRBEAQhbIghIgiCIAhC2IhaQyQhIQEPP/wwEhISwr2UkCHXHB3INQ9/ou16Abnm4UxEJ6sKgiAIgjC8iVqPiCAIgiAI4UcMEUEQBEEQwoYYIoIgCIIghA0xRARBEARBCBtRaYj85je/QUFBARITEzFnzhxs2bIl3EsKGo888ghsNpvbn8mTJ4d7WQFl06ZNuPbaazF27FjYbDa8/vrrbv+vaRp++MMfIjc3F0lJSbjiiivw+eefh2exAcLsmu+4445B972srCw8iw0QK1euxOzZszFq1ChkZ2fj+uuvx549e9w0XV1duOeee5CZmYmRI0fixhtvxLFjx8K0Yv/hXPNll1026F4vW7YsTCv2n9/+9reYNm2aq4lXaWkp3nrrLdf/D7d7DJhf83C7x55EnSHyyiuv4P7778fDDz+Mbdu2Yfr06bjqqqvQ1NQU7qUFjfPPPx8NDQ2uP5s3bw73kgLK6dOnMX36dPzmN7/x+v8/+clP8Mtf/hLPPPMMPvzwQ4wYMQJXXXUVurq6QrzSwGF2zQBQVlbmdt9feumlEK4w8Lz//vu45557UFVVhQ0bNuDs2bO48sorcfr0aZfm29/+Nt588028+uqreP/993H06FHccMMNYVy1f3CuGQDKy8vd7vVPfvKTMK3Yf/Ly8vDkk09i69at+Pjjj7Fw4UJcd911+PTTTwEMv3sMmF8zMLzu8SC0KOOiiy7S7rnnHte/+/r6tLFjx2orV64M46qCx8MPP6xNnz493MsIGQC0v/3tb65/OxwOLScnR3v66addP2tra9MSEhK0l156KQwrDDye16xpmnb77bdr1113XVjWEyqampo0ANr777+vaZrzvsbFxWmvvvqqS1NbW6sB0CorK8O1zIDiec2apmmXXnqpdt9994VvUSEgPT1d+93vfhcV95iga9a04X+Po8oj0tPTg61bt+KKK65w/cxut+OKK65AZWVlGFcWXD7//HOMHTsWRUVF+NrXvoaDBw+Ge0kho76+Ho2NjW73PDU1FXPmzBnW9xwA3nvvPWRnZ2PSpEm4++670dLSEu4lBZT29nYAQEZGBgBg69atOHv2rNu9njx5MiZMmDBs7rXnNRP/93//h6ysLJSUlGDFihXo7OwMx/ICTl9fH15++WWcPn0apaWlUXGPPa+ZGK73GIjwoXeBprm5GX19fRgzZozbz8eMGYPdu3eHaVXBZc6cOfjDH/6ASZMmoaGhAY8++ijmz5+P6upqjBo1KtzLCzqNjY0A4PWe0/8NR8rKynDDDTegsLAQdXV1+N73voerr74alZWViImJCffy/MbhcOA///M/cckll6CkpASA817Hx8cjLS3NTTtc7rW3awaAr371q8jPz8fYsWPxySef4MEHH8SePXvw2muvhXG1/rFr1y6Ulpaiq6sLI0eOxN/+9jcUFxdjx44dw/Ye+7pmYHjeYz1RZYhEI1dffbXr79OmTcOcOXOQn5+PP//5z7jzzjvDuDIhmHzlK19x/X3q1KmYNm0aJk6ciPfeew+XX355GFcWGO655x5UV1cPu3wnI3xd81133eX6+9SpU5Gbm4vLL78cdXV1mDhxYqiXGRAmTZqEHTt2oL29HX/5y19w++234/333w/3soKKr2suLi4elvdYT1SFZrKyshATEzMow/rYsWPIyckJ06pCS1paGs477zzs3bs33EsJCXRfo/meA0BRURGysrKGxX2/99578fe//x3vvvsu8vLyXD/PyclBT08P2tra3PTD4V77umZvzJkzBwCG9L2Oj4/HOeecg1mzZmHlypWYPn06fvGLXwzre+zrmr0xHO6xnqgyROLj4zFr1iy88847rp85HA688847brG44cypU6dQV1eH3NzccC8lJBQWFiInJ8ftnnd0dODDDz+MmnsOAIcPH0ZLS8uQvu+apuHee+/F3/72N2zcuBGFhYVu/z9r1izExcW53es9e/bg4MGDQ/Zem12zN3bs2AEAQ/pee+JwONDd3T0s77Ev6Jq9MezucbizZUPNyy+/rCUkJGh/+MMftJqaGu2uu+7S0tLStMbGxnAvLSg88MAD2nvvvafV19dr//rXv7QrrrhCy8rK0pqamsK9tIBx8uRJbfv27dr27ds1ANrPfvYzbfv27dqBAwc0TdO0J598UktLS9PeeOMN7ZNPPtGuu+46rbCwUDtz5kyYV24do2s+efKk9p3vfEerrKzU6uvrtX/+85/azJkztXPPPVfr6uoK99Itc/fdd2upqanae++9pzU0NLj+dHZ2ujTLli3TJkyYoG3cuFH7+OOPtdLSUq20tDSMq/YPs2veu3ev9thjj2kff/yxVl9fr73xxhtaUVGRtmDBgjCv3DrLly/X3n//fa2+vl775JNPtOXLl2s2m017++23NU0bfvdY04yveTjeY0+izhDRNE371a9+pU2YMEGLj4/XLrroIq2qqircSwoaN998s5abm6vFx8dr48aN026++WZt79694V5WQHn33Xc1AIP+3H777ZqmOUt4H3roIW3MmDFaQkKCdvnll2t79uwJ76L9xOiaOzs7tSuvvFIbPXq0FhcXp+Xn52vl5eVD3tj2dr0AtOeff96lOXPmjPbNb35TS09P15KTk7UvfelLWkNDQ/gW7Sdm13zw4EFtwYIFWkZGhpaQkKCdc8452ne/+12tvb09vAv3g3//93/X8vPztfj4eG306NHa5Zdf7jJCNG343WNNM77m4XiPPbFpmqaFzv8iCIIgCIIwQFTliAiCIAiCEFmIISIIgiAIQtgQQ0QQBEEQhLAhhoggCIIgCGFDDBFBEARBEMKGGCKCIAiCIIQNMUQEQRAEQQgbYogIgiAIghA2xBARBEEQBCFsiCEiCIIgCELYEENEEARBEISwIYaIIAiCIAhh4/8HhOj65A1z8sMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if u in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for OH\n",
      "In year/election 2020 Dem and GOP total votes were 2678283 3154724 for a stateGOP of 0.54084\n",
      "There are a total of 8941  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 2392589 2840665 for a stateGOP of 0.54281\n",
      "There are a total of 8941  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_vtd.csv\")\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for OH\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv OH8941legislPatchedVTDs.csv\n",
      "enter the number of HD-drawn districts in the state; e.g. 8 99\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 8941 drawn districts.  This state has 99 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of enacted districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = [ str( tractPopFile['GEOID20'][i]) for i in range(len(tractPopFile['GEOID20'])) ] #force strings to match\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "mergedGEO = [str(mergedDF['GEOID20'][i]) for i in range(len(mergedDF['GEOID20'])) ]  #force strings to match\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedGEO, \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Dem-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Dem-leaning vtds for year\",years[0])  #for MA, do GOP-leaning.  Other states, use Dem-leaning\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] > RepVotes[0][vestID[v]]:\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"D\", color = \"blue\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "working on drawn district no 3000\n",
      "working on drawn district no 4000\n",
      "working on drawn district no 5000\n",
      "working on drawn district no 6000\n",
      "working on drawn district no 7000\n",
      "working on drawn district no 8000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 2678283 3154724 476441\n",
      "ensemble : 2676577 3163839 487261\n",
      "the true and ensemble state GOP leans are 0.54084 0.5417146428839519\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total drawn districts, HD-drawn, map districts 8941 8941 99\n"
     ]
    }
   ],
   "source": [
    "print(\"total drawn districts, HD-drawn, map districts\",nDrawnDistricts,nHDs, nMapDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for funsies, let's plot some districts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"for funsies, let's plot some districts\")\n",
    "nSkip = 1000\n",
    "nStart = 487\n",
    "for t in range(nStart, nHDs,nSkip):\n",
    "    geo = tractGeom[HDvtdList[t][0]]\n",
    "    for v in HDvtdList[t]:\n",
    "        geo = geo.union(tractGeom[v])\n",
    "    plotPoly(geo)\n",
    "    plotPoly(hdCP[t].buffer(0.02))\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "2d545606-ca4c-4473-897c-992ca2ac3f58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "working on drawn district no 3000\n",
      "working on drawn district no 4000\n",
      "working on drawn district no 5000\n",
      "working on drawn district no 6000\n",
      "working on drawn district no 7000\n",
      "working on drawn district no 8000\n",
      "The GOP should win 0.628496224272884 fraction of seats at GOP vote of  0.54084 0.54281\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDvGOP = [0.5]*nHDs\n",
    "GOPseatFrac = 0.\n",
    "for t in range(nHDs):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    districtDems = np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ] )  + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])\n",
    "    districtReps = np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ] )  + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])\n",
    "    if districtDems > 0:\n",
    "        HDvGOP[t] = districtReps / (districtDems + districtReps)\n",
    "        GOPseatFrac += HDweight[t] * norm.cdf((HDvGOP[t] - 0.5 ) / 0.01)\n",
    "plt.hist(HDvGOP,weights=HDweight,bins=20)\n",
    "print(\"The GOP should win\",GOPseatFrac,\"fraction of seats at GOP vote of \",r5(vestReps[0]/(vestReps[0]+vestDems[0])),\n",
    "      r5(vestReps[1]/(vestReps[1]+vestDems[1])) )\n",
    "                                               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1feff378-e3c4-44c4-ac3e-7c0e6ad3ed57",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
